Accelerator for matrix decomposition

ABSTRACT

Systems and methods for a hardware accelerated matrix decomposition matrix decomposition circuit are described herein. This matrix decomposition circuit splits matrix decomposition operations into parallel operation circuits and serial operation circuits, and joins the parallel and serial operation circuits using specific dependency handling logic for efficient parallel execution. This provides fast matrix decomposition with low power consumption, reduced memory footprint, and reduced memory bandwidth.

TECHNICAL FIELD

Embodiments described herein generally relate to computer hardwarearchitecture, and more specifically to an accelerator for matrixdecomposition.

BACKGROUND

Increasingly, there is a demand for solutions to complex linear systems.These solutions are used in various applications, such as computervision (simultaneous localization and mapping (SLAM), robotics, dronesetc.), machine learning, control-systems, big-data analytics, and otherapplications. The solutions to these complex linear systems may includea matrix decomposition operation. Matrix decomposition operations arecomputationally intensive. For example, matrix decomposition complexitymay be cubic, such that, for N elements, processing involves N³computations. Such processing complexity often consumes substantialpower. Matrix decomposition operations also require substantial memorybandwidth, resulting in a substantial time delay in computing thesolution (e.g., large latency). The large latency may significantlyaffect the performance of various applications, such as slowing camerapose estimation or SLAM calculations. In addition to the power used inthe matrix decomposition mathematical operations, matrix decompositionoperations also require substantial energy to execute the large numberof memory accesses. When implemented on a software kernel running on ageneral purpose processor (e.g., central processing unit (CPU)), thematrix decomposition operations include unorganized memory accesspatterns (e.g., for triangular matrices) and serial operationdependencies, which further increase latency and power consumption. Thehigh latency and high energy consumption may substantially reduce theperformance of time-dependent applications, such as AR or VRapplications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a first matrix decomposition block diagram accordingto an embodiment.

FIG. 2 illustrates a second matrix decomposition block diagram accordingto an embodiment.

FIG. 3 is a column execution time graph according to an embodiment.

FIGS. 4A-4B are block diagrams illustrating a matrix memory layoutaccording to an embodiment.

FIGS. 5A-5B are block diagrams illustrating single-column matrixcomputations according to an embodiment.

FIGS. 6A-6B are block diagrams illustrating dual-column matrixcomputations according to an embodiment.

FIG. 7 is a block diagram of a matrix decomposition matrix decompositioncircuit, in accordance with at least one embodiment.

FIG. 8 is a block diagram of a matrix decomposition functional statemachine, in accordance with at least one embodiment.

FIG. 9 is a cycle count reduction graph, in accordance with at least oneembodiment.

FIG. 10 is a block diagram of a matrix decomposition method, inaccordance with at least one embodiment.

FIG. 11 is a block diagram illustrating a matrix decomposition matrixdecomposition circuit implemented in the example form of an electronicdevice, within which a set or sequence of instructions may be executedto cause the machine to perform any one of the methodologies discussedherein, according to an example embodiment.

FIGS. 12A-12B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according to anembodiment.

FIG. 13A-13D are block diagrams illustrating an example specific vectorfriendly instruction format according to an embodiment.

FIG. 14 is a block diagram of a register architecture according to oneembodiment of the invention.

FIG. 15A is a block diagram illustrating both an example in-orderpipeline and an example register renaming, out-of-order issue/executionpipeline according to an embodiment.

FIG. 15B is a block diagram illustrating both an example embodiment ofan in-order architecture core and an example register renaming,out-of-order issue/execution architecture core to be included in aprocessor according to an embodiment.

FIG. 16A-16B illustrate a block diagram of a more specific examplein-order core architecture, which core would be one of several logiccircuits (including other cores of the same type and/or different types)in a chip.

FIG. 17 is a block diagram of a processor that may have more than onecore, may have an integrated memory controller, and may have integratedgraphics according to an embodiment.

FIG. 18 is a block diagram of a system in accordance with oneembodiment.

FIG. 19 is a block diagram of a first more specific example system inaccordance with an embodiment.

FIG. 20 is a block diagram of a second more specific example system inaccordance with an embodiment.

FIG. 21 is a block diagram of a SoC in accordance with an embodiment.

FIG. 22 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to anembodiment.

DESCRIPTION OF EMBODIMENTS

A solution to the technical problems facing matrix decompositionoperations includes a hardware-accelerated solution for matrixdecomposition operations. In an embodiment, this hardware-acceleratedmatrix decomposition solution may be implemented on a hardwareaccelerated optimized micro-architecture (i.e. specialized circuitry).This hardware-accelerated matrix decomposition embodiment splits matrixdecomposition operations into parallel operation circuits (e.g., forhigh-bandwidth dot product operations) and serial operation circuits(e.g., for high-latency operations), and joins the parallel and serialoperation circuits using dependency handling logic for efficientparallel execution. This provides fast and low power matrixdecomposition with a reduced memory footprint and a reduced memorybandwidth.

This hardware-accelerated matrix decomposition embodiment providesvarious features. This hardware-accelerated matrix decompositionprovides matrix decomposition that is suitable for various decompositiontechniques, such as Takagi decomposition (e.g., for symmetric squarematrices) or Cholesky decomposition (e.g., for symmetric positivedefinite matrices). The hardware-accelerated matrix decompositionseparates and manages high-latency serial operations and high-bandwidthdot product operations for efficient parallel execution, such as shownin FIGS. 1-2. Additionally, despite a serial dependency in alternativematrix decomposition computations, the hardware-accelerated matrixdecomposition improves scaling of computations based on larger matricesthrough the use of improved dependency clear circuit, such as shown inFIGS. 1-2. The hardware-accelerated matrix decomposition embodimentreduces memory footprint by providing the ability to read an input froma memory and write the output matrix elements in-place back to the samememory. The embodiment provides the ability to read or write matrix datain triangular storage format or compressed linear storage format, suchas shown in FIGS. 4A-4B. The embodiment reduces memory footprint byproviding the ability to read an input from a memory and write theoutput matrix elements back to the same memory (e.g., in-place storage).

The hardware-accelerated matrix decomposition embodiment provides ascalable design to achieve lower latency and higher throughput withreduced memory bandwidth (e.g., using fewer memory accesses), such asshown in FIGS. 5A-6B. The embodiment provides improved computationmanagement and local buffering, which reduces memory access andincreases performance, such as shown in FIGS. 5A-6B. The embodimentprovides support for partial execution (e.g., stop and resumefunctionality), which provides the ability to manage pre-emption andsoftware and host controllability for large dimension matrixdecomposition, such as shown in FIG. 7. The embodiment also provides aconfigurable design for varying memory interface bit-width and computeresources, such as shown in FIG. 7.

The following description and the drawings illustrate exampleembodiments, though other embodiments may incorporate structural,logical, electrical, process, and other changes. Portions and featuresof various embodiments may be included in, or substituted for, those ofother embodiments. Embodiments set forth in the claims encompass allavailable equivalents of those claims.

FIG. 1 is a block diagram illustrating a first matrix decompositionsystem 100 according to an embodiment. Block diagram 100 shows a firstexample of dependency-clearing circuit for Cholesky matrixdecomposition. The dependency clear circuit may provide an indicationthat the dependency is clear, thus clearing the pipeline for the nextoperation. The Cholesky decomposition is useful to solve linearequations. For example, if matrix A is a symmetric and positive-definitematrix, then linear equation A x=B may be solved by first computing thedecomposition A=LL^(T), then solving L y=B by forward substitution, andfinally solving L^(T)x=y by backward substitution. This Choleskydecomposition determines elements for each row index i and column indexj. A Cholesky decomposition of A=LL^(T) includes determining elements ofmatrix L as follows:

$\begin{matrix}{{{for}\mspace{14mu} i} = {j\text{:}}} & {L_{j,j} = \sqrt{A_{j,j} - {\sum\limits_{k = 1}^{j - 1}L_{j,k}^{2}}}} \\{{{for}\mspace{14mu} i} < {j\text{:}}} & {L_{i,j} = {\frac{1}{L_{j,j}}\left( {A_{i,j} - {\sum\limits_{k = 1}^{j - 1}{L_{i,k}L_{j,k}}}} \right)}} \\{{{for}\mspace{14mu} i} > {j\text{:}}} & {L_{i,j} = 0}\end{matrix}$

This Cholesky decomposition assume the matrix to be lower triangular,which is captured for i>j above.

Another technique to decompose (e.g., factorize) a complex symmetricmatrix is Takagi's factorization (e.g., Takagi decomposition). If matrixA is a complex symmetric matrix, its Takagi factorization has the formA=VDV^(T). Matrix V is a unitary matrix, and the columns of matrix V maybe referred to as Takagi vectors. Matrix D is a diagonal singular valuematrix, and the elements of matrix D may be referred to as D_(i,j) foreach row index i and column index j. Elements of matrix V may bedetermined as follows:

$\begin{matrix}{{{for}\mspace{14mu} i} = {j\text{:}}} & {D_{j,j} = {A_{j,j} - {\sum\limits_{k = 1}^{j - 1}V_{j,k}^{2}}}} & {V_{i,j} = 1} \\{{{for}\mspace{14mu} i} < {j\text{:}}} & {D_{i,j} = 0} & {V_{i,j} = {\frac{1}{D_{j,j}}\left( {A_{i,j} - {\sum\limits_{k = 1}^{j - 1}{V_{i,k}V_{j,k}}}} \right)}} \\{{{for}\mspace{14mu} i} > {j\text{:}}} & {D_{i,j} = 0} & {V_{i,j} = 0}\end{matrix}$

The first matrix decomposition system 100 is described using a Choleskydecomposition, however a Takagi decomposition or other decomposition maybe used.

As shown in FIG. 1, block diagram 100 includes a Cholesky dot productoperation circuit 110 followed by a Cholesky subtraction and square rootor division operation circuit 115. In operation, the dot productoperation circuit 110 sends its output to the square root or divisionoperation circuit 115, which sends a dependency clear indication 105 toa wait for dependency clear circuit 120. The wait for dependency clearcircuit 120 may also send a hold indication 125 to another processingcircuit to hold further processing while the decomposition is beingexecuted. Because subsequent operations of the dot product operationcircuit 110 take inputs from the square root or division operationcircuit 115, the high-latency serial operation of the square root ordivision operation circuit 115 slows the overall progress of the matrixdecomposition.

FIG. 2 illustrates a second matrix decomposition block diagram 200according to an embodiment. Block diagram 200 includes a Cholesky dotproduct operation circuit 210 followed by a Cholesky subtraction andsquare root or division operation circuit 215. In addition, blockdiagram 200 also includes a partial dot product buffer 230 that retainsmultiple outputs of the dot product operation circuit 210, enablingmultiple high-bandwidth parallel operations. Because the square root ordivision operation circuit 215 involves a high-latency serial operation,circuit 215 may send a dependency clear indication 205 to a wait fordependency clear circuit 220, which may move appropriate data from thepartial dot product buffer 230 to a partial dot product serial buffer235 for the next operation of the square root or division operationcircuit 215. This configuration allows one or more dot product operationcircuits 210 to start computing the next set of input data for circuit215 while circuit 215 is completing earlier computations, thus reducingoverall latency. In an example, the one or more dot product operationcircuits 210 may be implemented as one or more multiply and accumulate(MAC) circuits, such as shown in FIG. 7.

FIG. 3 is a column execution time graph 300 according to an embodiment.Graph 300 shows matrix element computation time for an example matrix Athat includes 128×128 elements. The parallel and serial dependency shownin FIGS. 1-2 affect the latency of the operation. This dependency mayrestrict linear scaling of performance with parallel execution. Theseparation circuit shown in FIG. 2 improves the performance of theseoperations. More specifically, the computation separation matrixdecomposition circuit embodiment shown in FIG. 7 improves dependencymanagement and frees-up MAC circuits to start operations on new set ofdata while a computation-separation and serial-compute circuit isperforming operations on previous elements. The dependency may beimproved further by implementing multiple column processing shown inFIG. 7. As shown in graph 300, single column operations 310 may beimproved to dual-column naïve 320 (e.g., no dependency clear circuit),which may be further improved to dual-column optimized 330 (e.g., usingdependency clear circuit). In the example shown in FIG. 3, thedual-column naïve 320 may provide a 1.58× improvement over single columnoperations 310, and dual-column optimized 330 may provide a 1.96×improvement over single column operations 310.

FIGS. 4A-4B are block diagrams illustrating a matrix memory layout 400according to an embodiment. 4A shows a triangular storage format ofmemory for an upper triangular matrix A, including rows 410, 415, 420,425, 430, and 435. FIG. 4B shows the same elements of matrix A,including rows 410, 415, 420, 425, 430, and 435, stored in a compressedlinear format. The compressed linear storage of triangular memoryreduces the memory footprint and reduces power required for memorystorage and retrieval. The embodiment provides the ability to read frommemory or write to memory using triangular storage format or compressedlinear format. For example, an input matrix may be read from triangularstorage format and written in compressed linear format, or may be readfrom compressed linear storage format and written in triangular storageformat. The embodiment also provides in-place storage, such as theability to read from a memory address and write output data back to thatmemory for any combination of triangular storage format and compressedlinear storage format.

FIGS. 5A-5B are block diagrams illustrating single-column matrixcomputations 500 according to an embodiment. In particular, FIG. 5Ashows matrix element computation order, whereas FIG. 5B shows row bufferbased computation. The matrix decomposition performance is improved bytraversing matrix elements in such a way that input data may be reusedfor multiple operations. As discussed above, matrix decomposition may bedivided into parallel and serial operations. The parallel matrixdecomposition operations continue to calculate and accumulate dotproduct outputs, whereas serial matrix decomposition operationscalculate matrix element values using square-root or division (e.g.,based on element position).

In an example, the execution order of decomposition operations isselected to match with memory data layout. Column computation startsfrom a first column (e.g., a least column number) and continuesoperations on each subsequent column until reaching the end of matrix.Within each column, computation operations are traversed from firstdiagonal element 505 to last element of that column 530. As shown inFIG. 5A, elements may be grouped into 4 or 8 elements based on a widthof a memory read. For example, 128 bits may be grouped into 4 elements,or 256 bits may be grouped into 8 elements. As shown in FIG. 5A, thediagonal element 505 in a column is computed first followed bysubsequent elements in same column in the group of 4, where any circuitof fewer than 4 elements may be handled using a similar set of steps.FIG. 5B shows the pattern followed for dot product computation. Inparticular, FIG. 5B shows that a row corresponding to diagonal elementis locally buffered as row buffer data 510 while computing each diagonalelement, thus reducing memory reads. Each buffered row is thenmultiplied with each set of input data during column element computationoperations.

FIGS. 6A-6B are block diagrams illustrating dual-column matrixcomputations 600 according to an embodiment. In particular, FIG. 6Ashows dual-column computation order, whereas FIG. 6B shows row bufferbased computation. Similar to single-column matrix computations 500, theexecution order and matrix element grouping in dual-column matrixcomputations 600 is selected according to the memory data layout. Forexample, the grouping of elements within a column during dual-columnmatrix computations 600 is same as the grouping of elements within acolumn during single-column matrix computations 500. Unlikesingle-column matrix computations 500, two rows of data (e.g.,corresponding to two diagonal elements) are buffered during dual-columnmatrix computations 600. This buffering is performed during computationof a diagonal element, to reduce memory reads further.

As shown in FIG. 6A, a first diagonal element 605 in a column iscomputed first followed by a pairing 610 of a subsequent element of thatcolumn and the next diagonal element. A shortened dual-column 615includes two elements of the first column grouped with two elements ofnext column, and all four elements are computed in parallel. Forsubsequent operations, a standard dual-column 620 groups four elementsof a column with four elements of the next column, and all eightelements are computed in parallel.

The pattern operations followed for dot product computation is shown inFIG. 6B. Two rows corresponding to diagonal elements, such as row i 630and row i+1 635, are locally buffered while computing diagonal elementE_(ij), thus reducing memory reads. These two buffered rows are thenmultiplied with each set of input data during column element compute. Asshown in FIG. 6B, input data is being multiplied with row i 630 forcomputation of elements E(k,k+1,k+2,k+3) j 650, and also multiplied withrow i+1 635 for computation of elements E(k,k+1,k+2,k+3) j+1 655.

The dual-column matrix computations 600 provides substantialimprovements in performance, and reduces the number of memory accessesby almost half. As shown in FIG. 6B, a single input read data ismultiplied with two different row buffers 630 and 635 to calculatemultiple column elements. This dual-column matrix computations 600 alsoprovides the ability for a controller to fetch data only once for bothcolumns. In an example, when compared to approximately 98,000 memoryreads for decomposition of a 128×128 matrix in single column mode,dual-column matrix computations 600 require only approximately 53,000memory reads.

FIG. 7 is a block diagram of a hardware-accelerated matrix decompositioncircuit 700, in accordance with at least one embodiment. Matrixdecomposition circuit 700 includes a decomposition control circuit 710,a memory access control circuit 720, a parallel compute circuit 730, acomputation separation circuit 750, and a serial compute circuit 760.The decomposition control circuit 710 controls the sequencing of memoryfetch and compute operations, such as shown in and described withrespect to FIGS. 5A-6B. An example of this sequencing controlled by thedecomposition control circuit 710 is shown in FIG. 8 and describedbelow.

The memory access control circuit 720 manages reading data from memoryand writing data to memory 725 as directed by the decomposition controlcircuit 710. Memory 725 may include external memory, L2 cache memory, aninternal buffer, a last level cache (LLC), or other memory. This memoryaccess control circuit 720 handles address translation for triangularstorage format or compressed linear storage format, and handles writinggenerated data based on various decomposition requirements. For example,for Takagi decomposition, memory access control circuit 720 may writegenerated diagonal-only data. In an example, the address translationprovided by memory access control circuit 720 may be used to reduce amemory requirement to just over half of the original size, such as shownin and described with respect to FIGS. 4A-4B.

The parallel compute circuit 730 provides matrix decomposition parallelcomputing operations. The parallel compute circuit 730 includes variousmultiply-and-accumulate circuits, such as first MAC instruction circuit735 and second MAC instruction circuit 740. The parallel compute circuit730 performs per-column multiply-and-accumulate operations in parallelon multiple elements. In an example, the first MAC instruction circuit735 is used during single-column processing, and both the first MACinstruction circuit 735 and the second MAC instruction circuit 740together provide dual-column processing. In an example, processing ofadditional columns may be provided by a third MAC instruction circuit, afourth MAC instruction circuit, or additional MAC instruction circuits.The parallel compute circuit 730 also provides element biasing, whichfurther improves dependency management.

The computation separation circuit 750 manages dependency clear circuitand active MAC selection circuit. This is used to separate paralleloperations from serial operations, which provides improved performancescaling (e.g., near-linear performance scaling) with multiple-columnoperations. The computation separation circuit 750 receives MAC output(e.g., partial computed data) from the parallel compute circuit 730 andwaits for serial computation of previous-column data. Whenprevious-column data is received, the computation separation circuit 750merges the previous-column data with the MAC output and provides themerged output to the serial compute circuit 760. This allows MACcircuits within the parallel compute circuit 730 to begin paralleloperations on a subsequent set of data.

The serial compute circuit 760 provides serial computation operations.In particular, the serial compute circuit 760 performs MAC operationsfor dependent elements, which may be followed division operations orsquare root operations as required. This serial compute circuit 760 alsohandles writing of the inverse of the diagonal element.

The matrix decomposition circuit 700 may also include one or more rowbuffers 770. Each row buffer stores row data corresponding to a diagonalelement of a column that is currently undergoing computing operations.The row buffers 770 provide data to the parallel compute circuit 730,and may receive data from the memory circuit 725 through the memoryaccess control circuit 720. By using row buffers 770 to receive andstore data, the input data may be fetched only once from the memorycircuit 725, which reduces latency and power requirements associatedwith memory fetch operations. Additionally, ability to fetch memory onceprovides the ability to write output data in-place back to the samememory addresses, thereby reducing the hardware area used for memoryread and write operations. In addition, the row buffers 770 may beshared with any other compute circuit within matrix decompositioncircuit 700 or outside matrix decomposition circuit 700, furtherreducing the hardware area used for memory read and write operations.

The matrix decomposition circuit 700 provides the ability to writediagonal elements as an inverse of each element value, or to writewithout inverse. This provides additional flexibility during matrixdecomposition to avoid computationally intensive inverse operations,which may further reduce gate accesses and reduce latency. Matrixdecomposition circuit 700 provides the ability to use in-place memoryreplacement of elements of the decomposed matrix. This in-placereplacement further reduces system memory footprint. If needed, aseparate memory space may also be used to store the decomposed matrixelements. The matrix decomposition circuit 700 provides the ability tostart computing from any element of matrix, provided dependent elementsare populated in memory. This provides additional flexibility duringcomputations, such as providing the ability to save and restore variousmatrix calculation states. Matrix decomposition circuit 700 alsoprovides the ability to operate on either upper-triangular orlower-triangular matrices, and provides the ability to operate onmatrices stored in row-major or column-major format.

FIG. 8 is a block diagram of a matrix decomposition functional statemachine 800, in accordance with at least one embodiment. The functionalstate machine 800 shows various states of the execution flow for thematrix decomposition provided by matrix decomposition circuit 700. Thestate transitions within the functional state machine 800 may becontrolled by matrix decomposition circuit 700, such as shown in thedecomposition control circuit 710.

The functional state machine 800 begins by transitioning from an idlestate 810 to a compute diagonal state 820. When the decompositioncontrol circuit 710 determines that the last diagonal has not beencompleted, the state machine 800 transitions to a read all column state830. When all columns have been read, the state machine 800 transitionsto a wait diagonal done state 840. When the diagonal has been written tomemory, the state machine 800 transitions to a read all column elementsstate 850. All column elements are read, and column-specificcomputations are performed on all of the column elements. Once thecolumn-specific computations are completed, the state machine 800transitions to a next diagonal state 860, which increments the diagonalbeing computed and transitions back to the compute diagonal state 820.The processing loop within the state machine 800 may repeat foradditional diagonals, or may transition back to the idle state 810 whenthe last diagonal has been completed.

FIG. 9 is a cycle count reduction graph 900, in accordance with at leastone embodiment. Graph 900 includes a first set of data points 910corresponding to a first scale 915 showing the cycle count for a matrixdecomposition executed on a generic processor, such as on an ARM CortexA9 running at 1.2 GHz. Graph 900 includes a second set of data points920 corresponding to a second scale 925 showing the cycle count for amatrix decomposition executed using the hardware accelerated matrixdecomposition circuit. The matrix decomposition circuit provides asubstantial reduction in the cycle count. For example, for a 128×128matrix, the cycle count is reduced from approximately 879,000 cycles toapproximately 61,000 cycles. A comparison of the cycle count for a128×128 matrix is shown in Table 1 below:

TABLE 1 Cycle Count for 128 × 128 Matrix Platform Number Of CyclesIntel-Atom (CHT, 1.4 GHz) 502K ARM-Cortex-A9 (1.2 GHz) 879K Proposedsingle column solution 121K Proposed Dual column solution  61K

The matrix decomposition circuit requires very low power to operate. Inan example, matrix decomposition of 128×128 matrix using the matrixdecomposition circuit uses 47.55 mW at 600 MHz with TSMC 28 nm, nom, 1V,25C, with 79.25 pF. The matrix decomposition circuit also requires asmall footprint. In an example, the matrix decomposition circuit may beimplemented on 0.098 mm² for logic and 0.013 mm² RAM (e.g., shared RAM)at 600 MHz with TSMC 28LP, Pslow, 0.99V, 30C.

The matrix decomposition circuit provides improved speed and scaling. Inan example, the matrix decomposition circuit provides approximately 14×speed improvement compared to software implementation on a genericprocessor. In another example, this matrix decomposition circuit scalesapproximately linearly, such as requiring approximately 61K cycles fordual-column operations compared to approximately 121K cycles forsingle-column operations.

The matrix decomposition circuit also provides substantial reduction inmemory accesses. In an example, a generic processor executing a Choleskydecomposition for a 128×128 matrix size uses approximately 140K loadsand approximately 75K stores with 64-byte accesses, whereas the matrixdecomposition circuit reduces this to approximately 53K loads andapproximately 2.2K stores with only 16-byte accesses. This results in areduction of approximately 15× in memory access bandwidth.

FIG. 10 is a block diagram of a matrix decomposition method 1000, inaccordance with at least one embodiment. Method 1000 may includecalculating a serial computation output 1010. Calculating a serialcomputation output 1010 may include calculating a parallel computinginput 1012 at a memory access control circuit, calculating a parallelMAC single column output 1014 at a first multiply and accumulate (MAC)circuit within a parallel compute circuit, the parallel MAC singlecolumn output calculated based on the parallel computing input and afirst column input, calculating a serial computing input 1016 at acomputation separation circuit based on the parallel MAC single columnoutput, and calculating a serial computation output 1018 at a serialcompute circuit based on the serial computing input.

Method 1000 may include calculating a matrix decomposition control 1020at a decomposition control circuit. In an example, calculating theparallel computing input 1012 at the memory access control circuit isbased on the matrix decomposition control. In an example, the matrixdecomposition control is calculated based on the matrix decompositionconfiguration input. Method 1000 may include calculating a dependencyclear control 1030 at the computation separation circuit based on theparallel MAC single column output. In an example, calculating the serialcomputing input 1016 is further based on the dependency clear control.Method 1000 may include calculating a parallel MAC dual column output1040 at a second MAC circuit based on the second column input. Method1000 may include identifying an active parallel MAC output 1050 at thecomputation separation circuit based on the parallel MAC single columnoutput and the parallel MAC dual column output. In an example, thecomputation separation circuit calculating the serial computing input1016 is further based on the active parallel MAC output and the parallelMAC dual column output.

Method 1000 may include calculating a serial subtraction output 1060 ata serial subtraction circuit within the serial compute circuit based onthe serial computing input. In an example, the serial compute circuitcalculating the serial computation output is based on the serialsubtraction output. Method 1000 may include calculating a serial squareroot output 1070 at a square root circuit within the serial computecircuit based on the serial subtraction output. In an example, theserial compute circuit calculating the serial computation output isfurther based on the serial square root output. Method 1000 may includecalculating a serial inverse output 1080 at an inverse circuit withinthe serial compute circuit based on the serial subtraction output. In anexample, the serial compute circuit calculating the serial computationoutput is further based on the serial inverse output. Method 1000 mayinclude receiving the serial subtraction output 1090 at the memoryaccess control circuit and writing the serial subtraction output to amemory.

FIG. 11 is a block diagram illustrating a matrix decomposition matrixdecomposition circuit implemented in the example form of an electronicdevice 1100, within which a set or sequence of instructions may beexecuted to cause the machine to perform any one of the methodologiesdiscussed herein, according to an example embodiment. Electronic device1100 may also represent the devices shown in FIG. 7. In alternativeembodiments, the electronic device 1100 operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the electronic device 1100 may operate in the capacity ofeither a server or a client machine in server-client networkenvironments, or it may act as a peer machine in peer-to-peer (ordistributed) network environments. The electronic device 1100 may be anintegrated circuit (IC), a portable electronic device, a personalcomputer (PC), a tablet PC, a hybrid tablet, a personal digitalassistant (PDA), a mobile telephone, or any electronic device 1100capable of executing instructions (sequential or otherwise) that specifyactions to be taken by that machine to detect a user input. Further,while only a single electronic device 1100 is illustrated, the terms“machine” or “electronic device” shall also be taken to include anycollection of machines or devices that individually or jointly execute aset (or multiple sets) of instructions to perform any one or more of themethodologies discussed herein. Similarly, the term “processor-basedsystem” shall be taken to include any set of one or more machines thatare controlled by or operated by a processor (e.g., a computer) toexecute instructions, individually or jointly, to perform any one ormore of the methodologies discussed herein.

Example electronic device 1100 includes at least one processor 1102(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both, processor cores, compute nodes, etc.), a main memory 1104 and astatic memory 1106, which communicate with each other via a link 1108(e.g., bus).

The electronic device 1100 includes matrix decomposition hardware 1110,where the matrix decomposition hardware 1110 may include the componentsdescribed above in FIG. 7. The electronic device 1100 may furtherinclude a display unit 1112, where the display unit 1112 may include asingle component that provides a user-readable display and a protectivelayer, or another display type. The electronic device 1100 may furtherinclude an input device 1114, such as a pushbutton, a keyboard, an NFCcard reader, or a user interface (UI) navigation device (e.g., atouch-sensitive input). The electronic device 1100 may additionallyinclude a storage device 1116, such as a solid-state drive (SSD) unit.The electronic device 1100 may additionally include a signal generationdevice 1118 to provide audible or visual feedback, such as a speaker toprovide an audible feedback or one or more LEDs to provide a visualfeedback. The electronic device 1100 may additionally include a networkinterface device 1120, and one or more additional sensors (not shown),such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor.

The storage device 1116 includes a machine-readable medium 1122 on whichis stored one or more sets of data structures and instructions 1124(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1124 mayalso reside, completely or at least partially, within the main memory1104, static memory 1106, and/or within the processor 1102 duringexecution thereof by the electronic device 1100. The main memory 1104,static memory 1106, and the processor 1102 may also constitutemachine-readable media.

While the machine-readable medium 1122 is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions 1124. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present disclosure or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including but not limited to, by way ofexample, semiconductor memory devices (e.g., electrically programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM)) and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 1124 may further be transmitted or received over acommunications network 1126 using a transmission medium via the networkinterface device 1120 utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networksinclude a local area network (LAN), a wide area network (WAN), theInternet, mobile telephone networks, and wireless data networks (e.g.,Wi-Fi, NFC, Bluetooth, Bluetooth LE, 3G, 5G LTE/LTE-A, WiMAX networks,etc.). The term “transmission medium” shall be taken to include anyintangible medium that is capable of storing, encoding, or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such software.

The figures below detail example architectures and systems to implementembodiments of the above. In some embodiments, one or more hardwarecomponents and/or instructions described above are emulated as detailedbelow, or implemented as software modules.

Embodiments of the instruction(s) detailed above are embodied may beembodied in a “generic vector friendly instruction format” which isdetailed below. In other embodiments, such a format is not utilized andanother instruction format is used, however, the description below ofthe writemask registers, various data transformations (swizzle,broadcast, etc.), addressing, etc. is generally applicable to thedescription of the embodiments of the instruction(s) above.Additionally, example systems, architectures, and pipelines are detailedbelow. Embodiments of the instruction(s) above may be executed on suchsystems, architectures, and pipelines, but are not limited to thosedetailed.

An instruction set may include one or more instruction formats. A giveninstruction format may define various fields (e.g., number of bits,location of bits) to specify, among other things, the operation to beperformed (e.g., opcode) and the operand(s) on which that operation isto be performed and/or other data field(s) (e.g., mask). Someinstruction formats are further broken down though the definition ofinstruction templates (or subformats). For example, the instructiontemplates of a given instruction format may be defined to have differentsubsets of the instruction format's fields (the included fields aretypically in the same order, but at least some have different bitpositions because there are less fields included) and/or defined to havea given field interpreted differently. Thus, each instruction of an ISAis expressed using a given instruction format (and, if defined, in agiven one of the instruction templates of that instruction format) andincludes fields for specifying the operation and the operands. Forexample, an example ADD instruction has a specific opcode and aninstruction format that includes an opcode field to specify that opcodeand operand fields to select operands (source1/destination and source2);and an occurrence of this ADD instruction in an instruction stream willhave specific contents in the operand fields that select specificoperands. A set of SIMD extensions referred to as the Advanced VectorExtensions (AVX) (AVX1 and AVX2) and using the Vector Extensions (VEX)coding scheme has been released and/or published (e.g., see Intel® 64and IA-32 Architectures Software Developer's Manual, September 2014; andsee Intel® Advanced Vector Extensions Programming Reference, October2014).

Example Instruction Formats

Embodiments of the instruction(s) described herein may be embodied indifferent formats. Additionally, example systems, architectures, andpipelines are detailed below. Embodiments of the instruction(s) may beexecuted on such systems, architectures, and pipelines, but are notlimited to those detailed.

Generic Vector Friendly Instruction Format

A vector friendly instruction format is an instruction format that issuited for vector instructions (e.g., there are certain fields specificto vector operations). While embodiments are described in which bothvector and scalar operations are supported through the vector friendlyinstruction format, alternative embodiments use only vector operationsthe vector friendly instruction format.

FIGS. 12A-12B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according to anembodiment. FIG. 12A is a block diagram illustrating a generic vectorfriendly instruction format and class A instruction templates thereofaccording to embodiments of the invention; while FIG. 12B is a blockdiagram illustrating the generic vector friendly instruction format andclass B instruction templates thereof according to an embodiment.Specifically, a generic vector friendly instruction format 1200 forwhich are defined class A and class B instruction templates, both ofwhich include no memory access 1205 instruction templates and memoryaccess 1220 instruction templates. The term generic in the context ofthe vector friendly instruction format refers to the instruction formatnot being tied to any specific instruction set.

While embodiments of the invention will be described in which the vectorfriendly instruction format supports the following: a 64 byte vectoroperand length (or size) with 32 bit (4 byte) or 64 bit (8 byte) dataelement widths (or sizes) (and thus, a 64 byte vector consists of either16 doubleword-size elements or alternatively, 8 quadword-size elements);a 64 byte vector operand length (or size) with 16 bit (2 byte) or 8 bit(1 byte) data element widths (or sizes); a 32 byte vector operand length(or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit (2 byte), or 8bit (1 byte) data element widths (or sizes); and a 16 byte vectoroperand length (or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit(2 byte), or 8 bit (1 byte) data element widths (or sizes); alternativeembodiments may support more, less and/or different vector operand sizes(e.g., 256 byte vector operands) with more, less, or different dataelement widths (e.g., 128 bit (16 byte) data element widths).

The class A instruction templates in FIG. 12A include: 1) within the nomemory access 1205 instruction templates there is shown a no memoryaccess, full round control type operation 1210 instruction template anda no memory access, data transform type operation 1215 instructiontemplate; and 2) within the memory access 1220 instruction templatesthere is shown a memory access, temporal 1225 instruction template and amemory access, non-temporal 1230 instruction template. The class Binstruction templates in FIG. 12B include: 1) within the no memoryaccess 1205 instruction templates there is shown a no memory access,write mask control, partial round control type operation 1212instruction template and a no memory access, write mask control, VSIZEtype operation 1217 instruction template; and 2) within the memoryaccess 1220 instruction templates there is shown a memory access, writemask control 1227 instruction template.

The generic vector friendly instruction format 1200 includes thefollowing fields listed below in the order illustrated in FIGS. 12A-12B.

Format field 1240—a specific value (an instruction format identifiervalue) in this field uniquely identifies the vector friendly instructionformat, and thus occurrences of instructions in the vector friendlyinstruction format in instruction streams. As such, this field isoptional in the sense that it is not needed for an instruction set thathas only the generic vector friendly instruction format.

Base operation field 1242—its content distinguishes different baseoperations.

Register index field 1244—its content, directly or through addressgeneration, specifies the locations of the source and destinationoperands, be they in registers or in memory. These include a sufficientnumber of bits to select N registers from a PxQ (e.g. 32x512, 16x128,32x1024, 64x1024) register file. While in one embodiment N may be up tothree sources and one destination register, alternative embodiments maysupport more or less sources and destination registers (e.g., maysupport up to two sources where one of these sources also acts as thedestination, may support up to three sources where one of these sourcesalso acts as the destination, may support up to two sources and onedestination).

Modifier field 1246—its content distinguishes occurrences ofinstructions in the generic vector instruction format that specifymemory access from those that do not; that is, between no memory access1205 instruction templates and memory access 1220 instruction templates.Memory access operations read and/or write to the memory hierarchy (insome cases specifying the source and/or destination addresses usingvalues in registers), while non-memory access operations do not (e.g.,the source and destinations are registers). While in one embodiment thisfield also selects between three different ways to perform memoryaddress calculations, alternative embodiments may support more, less, ordifferent ways to perform memory address calculations.

Augmentation operation field 1250—its content distinguishes which one ofa variety of different operations to be performed in addition to thebase operation. This field is context specific. In one embodiment of theinvention, this field is divided into a class field 1268, an alpha field1252, and a beta field 1254. The augmentation operation field 1250allows common groups of operations to be performed in a singleinstruction rather than 2, 3, or 4 instructions.

Scale field 1260—its content allows for the scaling of the index field'scontent for memory address generation (e.g., for address generation thatuses 2^(scale)*index+base).

Displacement Field 1262A—its content is used as part of memory addressgeneration (e.g., for address generation that uses2^(scale)*index+base+displacement).

Displacement Factor Field 1262B (note that the juxtaposition ofdisplacement field 1262A directly over displacement factor field 1262Bindicates one or the other is used)—its content is used as part ofaddress generation; it specifies a displacement factor that is to bescaled by the size of a memory access (N)—where N is the number of bytesin the memory access (e.g., for address generation that uses2^(scale)*index+base+scaled displacement). Redundant low-order bits areignored and hence, the displacement factor field's content is multipliedby the memory operands total size (N) in order to generate the finaldisplacement to be used in calculating an effective address. The valueof N is determined by the processor hardware at runtime based on thefull opcode field 1274 (described later herein) and the datamanipulation field 1254C. The displacement field 1262A and thedisplacement factor field 1262B are optional in the sense that they arenot used for the no memory access 1205 instruction templates and/ordifferent embodiments may implement only one or none of the two.

Data element width field 1264—its content distinguishes which one of anumber of data element widths is to be used (in some embodiments for allinstructions; in other embodiments for only some of the instructions).This field is optional in the sense that it is not needed if only onedata element width is supported and/or data element widths are supportedusing some aspect of the opcodes.

Write mask field 1270—its content controls, on a per data elementposition basis, whether that data element position in the destinationvector operand reflects the result of the base operation andaugmentation operation. Class A instruction templates supportmerging-writemasking, while class B instruction templates support bothmerging- and zeroing-writemasking. When merging, vector masks allow anyset of elements in the destination to be protected from updates duringthe execution of any operation (specified by the base operation and theaugmentation operation); in other one embodiment, preserving the oldvalue of each element of the destination where the corresponding maskbit has a 0. In contrast, when zeroing vector masks allow any set ofelements in the destination to be zeroed during the execution of anyoperation (specified by the base operation and the augmentationoperation); in one embodiment, an element of the destination is set to 0when the corresponding mask bit has a 0 value. A subset of thisfunctionality is the ability to control the vector length of theoperation being performed (that is, the span of elements being modified,from the first to the last one); however, it is not necessary that theelements that are modified be consecutive. Thus, the write mask field1270 allows for partial vector operations, including loads, stores,arithmetic, logical, etc. While embodiments of the invention aredescribed in which the write mask field's 1270 content selects one of anumber of write mask registers that contains the write mask to be used(and thus the write mask field's 1270 content indirectly identifies thatmasking to be performed), alternative embodiments instead or additionalallow the mask write field's 1270 content to directly specify themasking to be performed.

Immediate field 1272—its content allows for the specification of animmediate. This field is optional in the sense that is it not present inan implementation of the generic vector friendly format that does notsupport immediate and it is not present in instructions that do not usean immediate.

Class field 1268—its content distinguishes between different classes ofinstructions. With reference to FIGS. 12A-B, the contents of this fieldselect between class A and class B instructions. In FIGS. 12A-B, roundedcorner squares are used to indicate a specific value is present in afield (e.g., class A 1268A and class B 1268B for the class field 1268respectively in FIGS. 12A-B).

Instruction Templates of Class A

In the case of the non-memory access 1205 instruction templates of classA, the alpha field 1252 is interpreted as an RS field 1252A, whosecontent distinguishes which one of the different augmentation operationtypes are to be performed (e.g., round 1252A.1 and data transform1252A.2 are respectively specified for the no memory access, round typeoperation 1210 and the no memory access, data transform type operation1215 instruction templates), while the beta field 1254 distinguisheswhich of the operations of the specified type is to be performed. In theno memory access 1205 instruction templates, the scale field 1260, thedisplacement field 1262A, and the displacement scale filed 1262B are notpresent.

No-Memory Access Instruction Templates—Full Round Control Type Operation

In the no memory access full round control type operation 1210instruction template, the beta field 1254 is interpreted as a roundcontrol field 1254A, whose content(s) provide static rounding. While inthe described embodiments of the invention the round control field 1254Aincludes a suppress all floating point exceptions (SAE) field 1256 and around operation control field 1258, alternative embodiments may supportmay encode both these concepts into the same field or only have one orthe other of these concepts/fields (e.g., may have only the roundoperation control field 1258).

SAE field 1256—its content distinguishes whether or not to disable theexception event reporting; when the SAE field's 1256 content indicatessuppression is enabled, a given instruction does not report any kind offloating-point exception flag and does not raise any floating pointexception handler.

Round operation control field 1258—its content distinguishes which oneof a group of rounding operations to perform (e.g., Round-up,Round-down, Round-towards-zero and Round-to-nearest). Thus, the roundoperation control field 1258 allows for the changing of the roundingmode on a per instruction basis. In one embodiment of the inventionwhere a processor includes a control register for specifying roundingmodes, the round operation control field's 1250 content overrides thatregister value.

No Memory Access Instruction Templates—Data Transform Type Operation

In the no memory access data transform type operation 1215 instructiontemplate, the beta field 1254 is interpreted as a data transform field1254B, whose content distinguishes which one of a number of datatransforms is to be performed (e.g., no data transform, swizzle,broadcast).

In the case of a memory access 1220 instruction template of class A, thealpha field 1252 is interpreted as an eviction hint field 1252B, whosecontent distinguishes which one of the eviction hints is to be used (inFIG. 12A, temporal 1252B.1 and non-temporal 1252B.2 are respectivelyspecified for the memory access, temporal 1225 instruction template andthe memory access, non-temporal 1230 instruction template), while thebeta field 1254 is interpreted as a data manipulation field 1254C, whosecontent distinguishes which one of a number of data manipulationoperations (also known as primitives) is to be performed (e.g., nomanipulation; broadcast; up conversion of a source; and down conversionof a destination). The memory access 1220 instruction templates includethe scale field 1260, and optionally the displacement field 1262A or thedisplacement scale field 1262B.

Vector memory instructions perform vector loads from and vector storesto memory, with conversion support. As with regular vector instructions,vector memory instructions transfer data from/to memory in a dataelement-wise fashion, with the elements that are actually transferred isdictated by the contents of the vector mask that is selected as thewrite mask.

Memory Access Instruction Templates—Temporal

Temporal data is data likely to be reused soon enough to benefit fromcaching. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Memory Access Instruction Templates—Non-Temporal

Non-temporal data is data unlikely to be reused soon enough to benefitfrom caching in the 1st-level cache and should be given priority foreviction. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Instruction Templates of Class B

In the case of the instruction templates of class B, the alpha field1252 is interpreted as a write mask control (Z) field 1252C, whosecontent distinguishes whether the write masking controlled by the writemask field 1270 should be a merging or a zeroing.

In the case of the non-memory access 1205 instruction templates of classB, part of the beta field 1254 is interpreted as an RL field 1257A,whose content distinguishes which one of the different augmentationoperation types are to be performed (e.g., round 1257A.1 and vectorlength (VSIZE) 1257A.2 are respectively specified for the no memoryaccess, write mask control, partial round control type operation 1212instruction template and the no memory access, write mask control, VSIZEtype operation 1217 instruction template), while the rest of the betafield 1254 distinguishes which of the operations of the specified typeis to be performed. In the no memory access 1205 instruction templates,the scale field 1260, the displacement field 1262A, and the displacementscale filed 1262B are not present.

In the no memory access, write mask control, partial round control typeoperation 1210 instruction template, the rest of the beta field 1254 isinterpreted as a round operation field 1259A and exception eventreporting is disabled (a given instruction does not report any kind offloating-point exception flag and does not raise any floating pointexception handler).

Round operation control field 1259A—just as round operation controlfield 1258, its content distinguishes which one of a group of roundingoperations to perform (e.g., Round-up, Round-down, Round-towards-zeroand Round-to-nearest). Thus, the round operation control field 1259Aallows for the changing of the rounding mode on a per instruction basis.In one embodiment of the invention where a processor includes a controlregister for specifying rounding modes, the round operation controlfield's 1250 content overrides that register value.

In the no memory access, write mask control, VSIZE type operation 1217instruction template, the rest of the beta field 1254 is interpreted asa vector length field 1259B, whose content distinguishes which one of anumber of data vector lengths is to be performed on (e.g., 128, 256, or512 byte).

In the case of a memory access 1220 instruction template of class B,part of the beta field 1254 is interpreted as a broadcast field 1257B,whose content distinguishes whether or not the broadcast type datamanipulation operation is to be performed, while the rest of the betafield 1254 is interpreted the vector length field 1259B. The memoryaccess 1220 instruction templates include the scale field 1260, andoptionally the displacement field 1262A or the displacement scale field1262B.

With regard to the generic vector friendly instruction format 1200, afull opcode field 1274 is shown including the format field 1240, thebase operation field 1242, and the data element width field 1264. Whileone embodiment is shown where the full opcode field 1274 includes all ofthese fields, the full opcode field 1274 includes less than all of thesefields in embodiments that do not support all of them. The full opcodefield 1274 provides the operation code (opcode).

The augmentation operation field 1250, the data element width field1264, and the write mask field 1270 allow these features to be specifiedon a per instruction basis in the generic vector friendly instructionformat.

The combination of write mask field and data element width field createtyped instructions in that they allow the mask to be applied based ondifferent data element widths.

The various instruction templates found within class A and class B arebeneficial in different situations. In some embodiments of theinvention, different processors or different cores within a processormay support only class A, only class B, or both classes. For instance, ahigh performance general purpose out-of-order core intended forgeneral-purpose computing may support only class B, a core intendedprimarily for graphics and/or scientific (throughput) computing maysupport only class A, and a core intended for both may support both (ofcourse, a core that has some mix of templates and instructions from bothclasses but not all templates and instructions from both classes iswithin the purview of the invention). Also, a single processor mayinclude multiple cores, all of which support the same class or in whichdifferent cores support different class. For instance, in a processorwith separate graphics and general purpose cores, one of the graphicscores intended primarily for graphics and/or scientific computing maysupport only class A, while one or more of the general purpose cores maybe high performance general purpose cores with out of order executionand register renaming intended for general-purpose computing thatsupport only class B. Another processor that does not have a separategraphics core, may include one more general purpose in-order orout-of-order cores that support both class A and class B. Of course,features from one class may also be implement in the other class indifferent embodiments of the invention. Programs written in a high levellanguage would be put (e.g., just in time compiled or staticallycompiled) into an variety of different executable forms, including: 1) aform having only instructions of the class(es) supported by the targetprocessor for execution; or 2) a form having alternative routineswritten using different combinations of the instructions of all classesand having control flow code that selects the routines to execute basedon the instructions supported by the processor which is currentlyexecuting the code.

Example Specific Vector Friendly Instruction Format

FIG. 13 is a block diagram illustrating an example specific vectorfriendly instruction format according to an embodiment. FIG. 13 shows aspecific vector friendly instruction format 1300 that is specific in thesense that it specifies the location, size, interpretation, and order ofthe fields, as well as values for some of those fields. The specificvector friendly instruction format 1300 may be used to extend the x86instruction set, and thus some of the fields are similar or the same asthose used in the existing x86 instruction set and extension thereof(e.g., AVX). This format remains consistent with the prefix encodingfield, real opcode byte field, MOD R/M field, SIB field, displacementfield, and immediate fields of the existing x86 instruction set withextensions. The fields from FIG. 12 into which the fields from FIG. 13map are illustrated.

It should be understood that, although embodiments of the invention aredescribed with reference to the specific vector friendly instructionformat 1300 in the context of the generic vector friendly instructionformat 1200 for illustrative purposes, the invention is not limited tothe specific vector friendly instruction format 1300 except whereclaimed. For example, the generic vector friendly instruction format1200 contemplates a variety of possible sizes for the various fields,while the specific vector friendly instruction format 1300 is shown ashaving fields of specific sizes. By way of specific example, while thedata element width field 1264 is illustrated as a one bit field in thespecific vector friendly instruction format 1300, the invention is notso limited (that is, the generic vector friendly instruction format 1200contemplates other sizes of the data element width field 1264).

The generic vector friendly instruction format 1200 includes thefollowing fields listed below in the order illustrated in FIG. 13A.

EVEX Prefix (Bytes 0-3) 1302—is encoded in a four-byte form.

Format Field 1240 (EVEX Byte 0, bits [7:0])—the first byte (EVEX Byte 0)is the format field 1240 and it contains 0x62 (the unique value used fordistinguishing the vector friendly instruction format in one embodimentof the invention).

The second-fourth bytes (EVEX Bytes 1-3) include a number of bit fieldsproviding specific capability.

REX field 1305 (EVEX Byte 1, bits [7-5])—consists of a EVEX.R bit field(EVEX Byte 1, bit [7]—R), EVEX.X bit field (EVEX byte 1, bit [6]—X), and1257BEX byte 1, bit[5]—B). The EVEX.R, EVEX.X, and EVEX.B bit fieldsprovide the same functionality as the corresponding VEX bit fields, andare encoded using is complement form, i.e. ZMM0 is encoded as 1111B,ZMM15 is encoded as 0000B. Other fields of the instructions encode thelower three bits of the register indexes as is known in the art (rrr,xxx, and bbb), so that Rrrr, Xxxx, and Bbbb may be formed by addingEVEX.R, EVEX.X, and EVEX.B.

REX′ field 1210—this is the first part of the REX′ field 1210 and is theEVEX.R′ bit field (EVEX Byte 1, bit [4]—R′) that is used to encodeeither the upper 16 or lower 16 of the extended 32 register set. In oneembodiment of the invention, this bit, along with others as indicatedbelow, is stored in bit inverted format to distinguish (in thewell-known x86 32-bit mode) from the BOUND instruction, whose realopcode byte is 62, but does not accept in the MOD R/M field (describedbelow) the value of 11 in the MOD field; alternative embodiments of theinvention do not store this and the other indicated bits below in theinverted format. A value of 1 is used to encode the lower 16 registers.In other words, R′Rrrr is formed by combining EVEX.R′, EVEX.R, and theother RRR from other fields.

Opcode map field 1315 (EVEX byte 1, bits [3:0]—mmmm)—its content encodesan implied leading opcode byte (0F, 0F 38, or 0F 3).

Data element width field 1264 (EVEX byte 2, bit [7]—W)—is represented bythe notation EVEX.W. EVEX.W is used to define the granularity (size) ofthe datatype (either 32-bit data elements or 64-bit data elements).

EVEX.vvvv 1320 (EVEX Byte 2, bits [6:3]-vvvv)—the role of EVEX.vvvv mayinclude the following: 1) EVEX.vvvv encodes the first source registeroperand, specified in inverted (1s complement) form and is valid forinstructions with 2 or more source operands; 2) EVEX.vvvv encodes thedestination register operand, specified in 1s complement form forcertain vector shifts; or 3) EVEX.vvvv does not encode any operand, thefield is reserved and should contain 1111b. Thus, EVEX.vvvv field 1320encodes the 4 low-order bits of the first source register specifierstored in inverted (1s complement) form. Depending on the instruction,an extra different EVEX bit field is used to extend the specifier sizeto 32 registers.

EVEX.U 1268 Class field (EVEX byte 2, bit [2]-U)—If EVEX.U=0, itindicates class A or EVEX.U0; if EVEX.U=1, it indicates class B orEVEX.U1.

Prefix encoding field 1325 (EVEX byte 2, bits [1:0]-pp)—providesadditional bits for the base operation field. In addition to providingsupport for the legacy SSE instructions in the EVEX prefix format, thisalso has the benefit of compacting the SIMD prefix (rather thanrequiring a byte to express the SIMD prefix, the EVEX prefix requiresonly 2 bits). In one embodiment, to support legacy SSE instructions thatuse a SIMD prefix (66H, F2H, F3H) in both the legacy format and in theEVEX prefix format, these legacy SIMD prefixes are encoded into the SIMDprefix encoding field; and at runtime are expanded into the legacy SIMDprefix prior to being provided to the decoder's PLA (so the PLA mayexecute both the legacy and EVEX format of these legacy instructionswithout modification). Although newer instructions could use the EVEXprefix encoding field's content directly as an opcode extension, certainembodiments expand in a similar fashion for consistency but allow fordifferent meanings to be specified by these legacy SIMD prefixes. Analternative embodiment may redesign the PLA to support the 2 bit SIMDprefix encodings, and thus not require the expansion.

Alpha field 1252 (EVEX byte 3, bit [7]—EH; also known as EVEX.EH,EVEX.rs, EVEX.RL, EVEX.write mask control, and EVEX.N; also illustratedwith α)—as previously described, this field is context specific.

Beta field 1254 (EVEX byte 3, bits [6:4]-SSS, also known as EVEX.s₂₋₀,EVEX.r₂₋₀, EVEX.rr1, EVEX.LL0, EVEX.LLB; also illustrated with PP(3)—aspreviously described, this field is context specific.

REX′ field 1210—this is the remainder of the REX′ field and is theEVEX.V′ bit field (EVEX Byte 3, bit [3]—V′) that may be used to encodeeither the upper 16 or lower 16 of the extended 32 register set. Thisbit is stored in bit inverted format. A value of 1 is used to encode thelower 16 registers. In other words, V′VVVV is formed by combiningEVEX.V′, EVEX.vvvv.

Write mask field 1270 (EVEX byte 3, bits [2:0]-kkk)—its contentspecifies the index of a register in the write mask registers aspreviously described. In one embodiment of the invention, the specificvalue EVEX.kkk=000 has a special behavior implying no write mask is usedfor the particular instruction (this may be implemented in a variety ofways including the use of a write mask hardwired to all ones or hardwarethat bypasses the masking hardware).

Real Opcode Field 1330 (Byte 4) is also known as the opcode byte. Partof the opcode is specified in this field.

MOD R/M Field 1340 (Byte 5) includes MOD field 1342, Reg field 1344, andR/M field 1346. As previously described, the MOD field's 1342 contentdistinguishes between memory access and non-memory access operations.The role of Reg field 1344 may be summarized to two situations: encodingeither the destination register operand or a source register operand, orbe treated as an opcode extension and not used to encode any instructionoperand. The role of RIM field 1346 may include the following: encodingthe instruction operand that references a memory address, or encodingeither the destination register operand or a source register operand.

Scale, Index, Base (SIB) Byte (Byte 6)—As previously described, thescale field's 1250 content is used for memory address generation.SIB.xxx 1354 and SIB.bbb 1356—the contents of these fields have beenpreviously referred to with regard to the register indexes Xxxx andBbbb.

Displacement field 1262A (Bytes 7-10)—when MOD field 1342 contains 10,bytes 7-10 are the displacement field 1262A, and it works the same asthe legacy 32-bit displacement (disp32) and works at byte granularity.

Displacement factor field 1262B (Byte 7)—when MOD field 1342 contains01, byte 7 is the displacement factor field 1262B. The location of thisfield is that same as that of the legacy x86 instruction set 8-bitdisplacement (disp8), which works at byte granularity. Since disp8 issign extended, it may only address between −128 and 127 bytes offsets;in terms of 64 byte cache lines, disp8 uses 8 bits that may be set toonly four really useful values −128, −64, 0, and 64; since a greaterrange is often needed, disp32 is used; however, disp32 requires 4 bytes.In contrast to disp8 and disp32, the displacement factor field 1262B isa reinterpretation of disp8; when using displacement factor field 1262B,the actual displacement is determined by the content of the displacementfactor field multiplied by the size of the memory operand access (N).This type of displacement is referred to as disp8*N. This reduces theaverage instruction length (a single byte of used for the displacementbut with a much greater range). Such compressed displacement is based onthe assumption that the effective displacement is multiple of thegranularity of the memory access, and hence, the redundant low-orderbits of the address offset do not need to be encoded. In other words,the displacement factor field 1262B substitutes the legacy x86instruction set 8-bit displacement. Thus, the displacement factor field1262B is encoded the same way as an x86 instruction set 8-bitdisplacement (so no changes in the ModRM/SIB encoding rules) with theonly exception that disp8 is overloaded to disp8*N. In other words,there are no changes in the encoding rules or encoding lengths but onlyin the interpretation of the displacement value by hardware (which needsto scale the displacement by the size of the memory operand to obtain abyte-wise address offset). Immediate field 1272 operates as previouslydescribed.

Full Opcode Field

FIG. 13B is a block diagram illustrating the fields of the specificvector friendly instruction format 1300 that make up the full opcodefield 1274 according to one embodiment of the invention. Specifically,the full opcode field 1274 includes the format field 1240, the baseoperation field 1242, and the data element width (W) field 1264. Thebase operation field 1242 includes the prefix encoding field 1325, theopcode map field 1315, and the real opcode field 1330.

Register Index Field

FIG. 13C is a block diagram illustrating the fields of the specificvector friendly instruction format 1300 that make up the register indexfield 1244 according to one embodiment of the invention. Specifically,the register index field 1244 includes the REX field 1305, the REX′field 1310, the MODR/M.reg field 1344, the MODR/M.r/m field 1346, theVVVV field 1320, xxx field 1354, and the bbb field 1356.

Augmentation Operation Field

FIG. 13D is a block diagram illustrating the fields of the specificvector friendly instruction format 1300 that make up the augmentationoperation field 1250 according to one embodiment of the invention. Whenthe class (U) field 1268 contains 0, it signifies EVEX.U0 (class A1268A); when it contains 1, it signifies EVEX.U1 (class B 1268B). WhenU=0 and the MOD field 1342 contains 11 (signifying a no memory accessoperation), the alpha field 1252 (EVEX byte 3, bit [7]—EH) isinterpreted as the rs field 1252A. When the rs field 1252A contains a 1(round 1252A.1), the beta field 1254 (EVEX byte 3, bits [6:4]—SSS) isinterpreted as the round control field 1254A. The round control field1254A includes a one bit SAE field 1256 and a two bit round operationfield 1258. When the rs field 1252A contains a 0 (data transform1252A.2), the beta field 1254 (EVEX byte 3, bits [6:4]—SSS) isinterpreted as a three bit data transform field 1254B. When U=0 and theMOD field 1342 contains 00, 01, or 10 (signifying a memory accessoperation), the alpha field 1252 (EVEX byte 3, bit [7]—EH) isinterpreted as the eviction hint (EH) field 1252B and the beta field1254 (EVEX byte 3, bits [6:4]—SSS) is interpreted as a three bit datamanipulation field 1254C.

When U=1, the alpha field 1252 (EVEX byte 3, bit [7]—EH) is interpretedas the write mask control (Z) field 1252C. When U=1 and the MOD field1342 contains 11 (signifying a no memory access operation), part of thebeta field 1254 (EVEX byte 3, bit [4]—S0) is interpreted as the RL field1257A; when it contains a 1 (round 1257A.1) the rest of the beta field1254 (EVEX byte 3, bit [6-5]—S2-1) is interpreted as the round operationfield 1259A, while when the RL field 1257A contains a 0 (VSIZE 1257.A2)the rest of the beta field 1254 (EVEX byte 3, bit [6-5]—S2-1) isinterpreted as the vector length field 1259B (EVEX byte 3, bit[6-5]—L1-0). When U=1 and the MOD field 1342 contains 00, 01, or 10(signifying a memory access operation), the beta field 1254 (EVEX byte3, bits [6:4]-SSS) is interpreted as the vector length field 1259B (EVEXbyte 3, bit [6-5]-L1-0) and the broadcast field 1257B (EVEX byte 3, bit[4]—B).

Example Register Architecture

FIG. 14 is a block diagram of a register architecture 1400 according toone embodiment of the invention. In the embodiment illustrated, thereare 32 vector registers 1410 that are 512 bits wide; these registers arereferenced as zmm0 through zmm31. The lower order 256 bits of the lower16 zmm registers are overlaid on registers ymm0-16. The lower order 128bits of the lower 16 zmm registers (the lower order 128 bits of the ymmregisters) are overlaid on registers xmm0-15. The specific vectorfriendly instruction format 1300 operates on these overlaid registerfile as illustrated in the below tables.

Adjustable Vector Length Class Operations Registers Instruction A 1210,1215, zmm registers (the vector Templates that (FIG. 12A; 1225, 1230length is 64 byte) do not include U = 0) the vector B 1212 zmm registers(the vector length field (FIG. 12B; length is 64 byte) 1259B U = 1)Instruction B 1217, 1227 zmm, ymm, or xmm templates that (FIG. 12B;registers (the vector length do include the U = 1) is 64 byte, 32 byte,or 16 vector length byte) depending on the field 1259B vector lengthfield 1259B

In other words, the vector length field 1259B selects between a maximumlength and one or more other shorter lengths, where each such shorterlength is half the length of the preceding length; and instructionstemplates without the vector length field 1259B operate on the maximumvector length. Further, in one embodiment, the class B instructiontemplates of the specific vector friendly instruction format 1300operate on packed or scalar single/double-precision floating point dataand packed or scalar integer data. Scalar operations are operationsperformed on the lowest order data element position in an zmm/ymm/xmmregister; the higher order data element positions are either left thesame as they were prior to the instruction or zeroed depending on theembodiment.

Write mask registers 1415—in the embodiment illustrated, there are 8write mask registers (k0 through k7), each 64 bits in size. In analternate embodiment, the write mask registers 1415 are 16 bits in size.As previously described, in one embodiment of the invention, the vectormask register k0 cannot be used as a write mask; when the encoding thatwould normally indicate k0 is used for a write mask, it selects ahardwired write mask of 0xFFFF, effectively disabling write masking forthat instruction.

General-purpose registers 1425—in the embodiment illustrated, there aresixteen 64-bit general-purpose registers that are used along with theexisting x86 addressing modes to address memory operands. Theseregisters are referenced by the names RAX, RBX, RCX, RDX, RBP, RSI, RDI,RSP, and R8 through R15.

Scalar floating point stack register file (x87 stack) 1445, on which isaliased the MMX packed integer flat register file 1450—in the embodimentillustrated, the x87 stack is an eight-element stack used to performscalar floating-point operations on 32/64/80-bit floating point datausing the x87 instruction set extension; while the MMX registers areused to perform operations on 64-bit packed integer data, as well as tohold operands for some operations performed between the MMX and XMMregisters.

Alternative embodiments of the invention may use wider or narrowerregisters. Additionally, alternative embodiments of the invention mayuse more, less, or different register files and registers.

Example Core Architectures, Processors, and Computer Architectures

Processor cores may be implemented in different ways, for differentpurposes, and in different processors. For instance, implementations ofsuch cores may include: 1) a general purpose in-order core intended forgeneral-purpose computing; 2) a high performance general purposeout-of-order core intended for general-purpose computing; 3) a specialpurpose core intended primarily for graphics and/or scientific(throughput) computing. Implementations of different processors mayinclude: 1) a CPU including one or more general purpose in-order coresintended for general-purpose computing and/or one or more generalpurpose out-of-order cores intended for general-purpose computing; and2) a coprocessor including one or more special purpose cores intendedprimarily for graphics and/or scientific (throughput). Such differentprocessors lead to different computer system architectures, which mayinclude: 1) the coprocessor on a separate chip from the CPU; 2) thecoprocessor on a separate die in the same package as a CPU; 3) thecoprocessor on the same die as a CPU (in which case, such a coprocessoris sometimes referred to as special purpose logic, such as integratedgraphics and/or scientific (throughput) logic, or as special purposecores); and 4) a system on a chip that may include on the same die thedescribed CPU (sometimes referred to as the application core(s) orapplication processor(s)), the above described coprocessor, andadditional functionality. Example core architectures are described next,followed by descriptions of example processors and computerarchitectures.

Example Core Architectures: In-Order and Out-of-Order Core Block Diagram

FIG. 15A is a block diagram illustrating both an example in-orderpipeline and an example register renaming, out-of-order issue/executionpipeline according to an embodiment. FIG. 15B is a block diagramillustrating both an example embodiment of an in-order architecture coreand an example register renaming, out-of-order issue/executionarchitecture core to be included in a processor according to anembodiment. The solid lined boxes in FIGS. 15A-B illustrate the in-orderpipeline and in-order core, while the optional addition of the dashedlined boxes illustrates the register renaming, out-of-orderissue/execution pipeline and core. Given that the in-order aspect is asubset of the out-of-order aspect, the out-of-order aspect will bedescribed.

In FIG. 15A, a processor pipeline 1500 includes a fetch stage 1502, alength decode stage 1504, a decode stage 1506, an allocation stage 1508,a renaming stage 1510, a scheduling (also known as a dispatch or issue)stage 1512, a register read/memory read stage 1514, an execute stage1516, a write back/memory write stage 1518, an exception handling stage1522, and a commit stage 1524.

FIG. 15B shows processor core 1590 including a front end unit 1530coupled to an execution engine unit 1550, and both are coupled to amemory unit 1570. The core 1590 may be a reduced instruction setcomputing (RISC) core, a complex instruction set computing (CISC) core,a very long instruction word (VLIW) core, or a hybrid or alternativecore type. As yet another option, the core 1590 may be a special-purposecore, such as, for example, a network or communication core, compressionengine, coprocessor core, general purpose computing graphics processingunit (GPGPU) core, graphics core, or the like.

The front end unit 1530 includes a branch prediction unit 1532 coupledto an instruction cache unit 1534, which is coupled to an instructiontranslation lookaside buffer (TLB) 1536, which is coupled to aninstruction fetch unit 1538, which is coupled to a decode unit 1540. Thedecode unit 1540 (or decoder) may decode instructions, and generate asan output one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichare decoded from, or which otherwise reflect, or are derived from, theoriginal instructions. The decode unit 1540 may be implemented usingvarious different mechanisms. Examples of suitable mechanisms include,but are not limited to, look-up tables, hardware implementations,programmable logic arrays (PLAs), microcode read only memories (ROMs),etc. In one embodiment, the core 1590 includes a microcode ROM or othermedium that stores microcode for certain macroinstructions (e.g., indecode unit 1540 or otherwise within the front end unit 1530). Thedecode unit 1540 is coupled to a rename/allocator unit 1552 in theexecution engine unit 1550.

The execution engine unit 1550 includes the rename/allocator unit 1552coupled to a retirement unit 1554 and a set of one or more schedulerunit(s) 1556. The scheduler unit(s) 1556 represents any number ofdifferent schedulers, including reservations stations, centralinstruction window, etc. The scheduler unit(s) 1556 is coupled to thephysical register file(s) unit(s) 1558. Each of the physical registerfile(s) units 1558 represents one or more physical register files,different ones of which store one or more different data types, such asscalar integer, scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point, status (e.g., aninstruction pointer that is the address of the next instruction to beexecuted), etc. In one embodiment, the physical register file(s) unit1558 comprises a vector registers unit, a write mask registers unit, anda scalar registers unit. These register units may provide architecturalvector registers, vector mask registers, and general purpose registers.The physical register file(s) unit(s) 1558 is overlapped by theretirement unit 1554 to illustrate various ways in which registerrenaming and out-of-order execution may be implemented (e.g., using areorder buffer(s) and a retirement register file(s); using a futurefile(s), a history buffer(s), and a retirement register file(s); using aregister maps and a pool of registers; etc.). The retirement unit 1554and the physical register file(s) unit(s) 1558 are coupled to theexecution cluster(s) 1560. The execution cluster(s) 1560 includes a setof one or more execution units 1562 and a set of one or more memoryaccess units 1564. The execution units 1562 may perform variousoperations (e.g., shifts, addition, subtraction, multiplication) and onvarious types of data (e.g., scalar floating point, packed integer,packed floating point, vector integer, vector floating point). Whilesome embodiments may include a number of execution units dedicated tospecific functions or sets of functions, other embodiments may includeonly one execution unit or multiple execution units that all perform allfunctions. The scheduler unit(s) 1556, physical register file(s) unit(s)1558, and execution cluster(s) 1560 are shown as being possibly pluralbecause certain embodiments create separate pipelines for certain typesof data/operations (e.g., a scalar integer pipeline, a scalar floatingpoint/packed integer/packed floating point/vector integer/vectorfloating point pipeline, and/or a memory access pipeline that each havetheir own scheduler unit, physical register file(s) unit, and/orexecution cluster—and in the case of a separate memory access pipeline,certain embodiments are implemented in which only the execution clusterof this pipeline has the memory access unit(s) 1564). It should also beunderstood that where separate pipelines are used, one or more of thesepipelines may be out-of-order issue/execution and the rest in-order.

The set of memory access units 1564 is coupled to the memory unit 1570,which includes a data TLB unit 1572 coupled to a data cache unit 1574coupled to a level 2 (L2) cache unit 1576. In one example embodiment,the memory access units 1564 may include a load unit, a store addressunit, and a store data unit, each of which is coupled to the data TLBunit 1572 in the memory unit 1570. The instruction cache unit 1534 isfurther coupled to a level 2 (L2) cache unit 1576 in the memory unit1570. The L2 cache unit 1576 is coupled to one or more other levels ofcache and eventually to a main memory.

By way of example, the example register renaming, out-of-orderissue/execution core architecture may implement the pipeline 1500 asfollows: 1) the instruction fetch 1538 performs the fetch and lengthdecoding stages 1502 and 1504; 2) the decode unit 1540 performs thedecode stage 1506; 3) the rename/allocator unit 1552 performs theallocation stage 1508 and renaming stage 1510; 4) the scheduler unit(s)1556 performs the schedule stage 1512; 5) the physical register file(s)unit(s) 1558 and the memory unit 1570 perform the register read/memoryread stage 1514; the execution cluster 1560 perform the execute stage1516; 6) the memory unit 1570 and the physical register file(s) unit(s)1558 perform the write back/memory write stage 1518; 7) various unitsmay be involved in the exception handling stage 1522; and 8) theretirement unit 1554 and the physical register file(s) unit(s) 1558perform the commit stage 1524.

The core 1590 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.), including theinstruction(s) described herein. In one embodiment, the core 1590includes logic to support a packed data instruction set extension (e.g.,AVX1, AVX2), thereby allowing the operations used by many multimediaapplications to be performed using packed data.

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor also includes separate instruction and data cache units1534/1574 and a shared L2 cache unit 1576, alternative embodiments mayhave a single internal cache for both instructions and data, such as,for example, a Level 1 (L1) internal cache, or multiple levels ofinternal cache. In some embodiments, the system may include acombination of an internal cache and an external cache that is externalto the core and/or the processor. Alternatively, all of the cache may beexternal to the core and/or the processor.

Specific Example In-Order Core Architecture

FIGS. 16A-16B illustrate a block diagram of a more specific examplein-order core architecture, which core would be one of several logiccircuits (including other cores of the same type and/or different types)in a chip. The logic circuits communicate through a high-bandwidthinterconnect network (e.g., a ring network) with some fixed functionlogic, memory I/O interfaces, and other necessary I/O logic, dependingon the application.

FIG. 16A is a block diagram of a single processor core, along with itsconnection to the on-die interconnect network 1602 and with its localsubset of the Level 2 (L2) cache 1604, according to an embodiment. Inone embodiment, an instruction decoder 1600 supports the x86 instructionset with a packed data instruction set extension. An L1 cache 1606allows low-latency accesses to cache memory into the scalar and vectorunits. While in one embodiment (to simplify the design), a scalar unit1608 and a vector unit 1610 use separate register sets (respectively,scalar registers 1612 and vector registers 1614) and data transferredbetween them is written to memory and then read back in from a level 1(L1) cache 1606, alternative embodiments of the invention may use adifferent approach (e.g., use a single register set or include acommunication path that allow data to be transferred between the tworegister files without being written and read back).

The local subset of the L2 cache 1604 is part of a global L2 cache thatis divided into separate local subsets, one per processor core. Eachprocessor core has a direct access path to its own local subset of theL2 cache 1604. Data read by a processor core is stored in its L2 cachesubset 1604 and may be accessed quickly, in parallel with otherprocessor cores accessing their own local L2 cache subsets. Data writtenby a processor core is stored in its own L2 cache subset 1604 and isflushed from other subsets, if necessary. The ring network ensurescoherency for shared data. The ring network is bi-directional to allowagents such as processor cores, L2 caches and other logic circuits tocommunicate with each other within the chip. Each ring data-path is1012-bits wide per direction.

FIG. 16B is an expanded view of part of the processor core in FIG. 16Aaccording to an embodiment. FIG. 16B includes an L1 data cache 1606Apart of the L1 cache 1604, as well as more detail regarding the vectorunit 1610 and the vector registers 1614. Specifically, the vector unit1610 is a 16-wide vector processing unit (VPU) (see the 16-wide ALU1628), which executes one or more of integer, single-precision float,and double-precision float instructions. The VPU supports swizzling theregister inputs with swizzle unit 1620, numeric conversion with numericconvert units 1622A-B, and replication with replication unit 1624 on thememory input. Write mask registers 1626 allow predicating resultingvector writes.

FIG. 17 is a block diagram of a processor 1700 that may have more thanone core, may have an integrated memory controller, and may haveintegrated graphics according to an embodiment. The solid lined boxes inFIG. 17 illustrate a processor 1700 with a single core 1702A, a systemagent 1710, a set of one or more bus controller units 1716, while theoptional addition of the dashed lined boxes illustrates an alternativeprocessor 1700 with multiple cores 1702A-N, a set of one or moreintegrated memory controller unit(s) 1714 in the system agent unit 1710,and special purpose circuit 1708.

Thus, different implementations of the processor 1700 may include: 1) aCPU with the special purpose circuit 1708 being integrated graphicsand/or scientific (throughput) circuit (which may include one or morecores), and the cores 1702A-N being one or more general purpose cores(e.g., general purpose in-order cores, general purpose out-of-ordercores, a combination of the two); 2) a coprocessor with the cores1702A-N being a large number of special purpose cores intended primarilyfor graphics and/or scientific (throughput); and 3) a coprocessor withthe cores 1702A-N being a large number of general purpose in-ordercores. Thus, the processor 1700 may be a general-purpose processor,coprocessor or special-purpose processor, such as, for example, anetwork or communication processor, compression engine, graphicsprocessor, GPGPU (general purpose graphics processing unit), ahigh-throughput many integrated core (MIC) coprocessor (including 30 ormore cores), embedded processor, or the like. The processor may beimplemented on one or more chips. The processor 1700 may be a part ofand/or may be implemented on one or more substrates using any of anumber of process technologies, such as, for example, BiCMOS, CMOS, orNMOS.

The memory hierarchy includes one or more levels of cache within thecores, a set or one or more shared cache units 1706, and external memory(not shown) coupled to the set of integrated memory controller units1714. The set of shared cache units 1706 may include one or moremid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), orother levels of cache, a last level cache (LLC), and/or combinationsthereof. While in one embodiment a ring based interconnect unit 1712interconnects the integrated graphics circuit 1708, the set of sharedcache units 1706, and the system agent unit 1710/integrated memorycontroller unit(s) 1714, alternative embodiments may use any number ofwell-known techniques for interconnecting such units. In one embodiment,coherency is maintained between one or more cache units 1706 and cores1702-A-N.

In some embodiments, one or more of the cores 1702A-N are capable ofmulti-threading. The system agent 1710 includes those componentscoordinating and operating cores 1702A-N. The system agent unit 1710 mayinclude for example a power control unit (PCU) and a display unit. ThePCU may be or include circuit and components needed for regulating thepower state of the cores 1702A-N and the integrated graphics circuit1708. The display unit is for driving one or more externally connecteddisplays.

The cores 1702A-N may be homogenous or heterogeneous in terms ofarchitecture instruction set; that is, two or more of the cores 1702A-Nmay be capable of execution the same instruction set, while others maybe capable of executing only a subset of that instruction set or adifferent instruction set.

Example Computer Architectures

FIGS. 18-21 are block diagrams of example computer architectures. Othersystem designs and configurations known in the arts for laptops,desktops, handheld PCs, personal digital assistants, engineeringworkstations, servers, network devices, network hubs, switches, embeddedprocessors, digital signal processors (DSPs), graphics devices, videogame devices, set-top boxes, micro controllers, cell phones, portablemedia players, hand held devices, and various other electronic devices,are also suitable. In general, a huge variety of systems or electronicdevices capable of incorporating a processor and/or other executioncircuit as disclosed herein are generally suitable.

Referring now to FIG. 18, shown is a block diagram of a system 1800 inaccordance with one embodiment of the present invention. The system 1800may include one or more processors 1810, 1815, which are coupled to acontroller hub 1820. In one embodiment the controller hub 1820 includesa graphics memory controller hub (GMCH) 1890 and an Input/Output Hub(IOH) 1850 (which may be on separate chips); the GMCH 1890 includesmemory and graphics controllers to which are coupled memory 1840 and acoprocessor 1845; the IOH 1850 is couples input/output (I/O) devices1860 to the GMCH 1890. Alternatively, one or both of the memory andgraphics controllers are integrated within the processor (as describedherein), the memory 1840 and the coprocessor 1845 are coupled directlyto the processor 1810, and the controller hub 1820 in a single chip withthe IOH 1850.

The optional nature of additional processors 1815 is denoted in FIG. 18with broken lines. Each processor 1810, 1815 may include one or more ofthe processing cores described herein and may be some version of theprocessor 1700.

The memory 1840 may be, for example, dynamic random access memory(DRAM), phase change memory (PCM), or a combination of the two. For atleast one embodiment, the controller hub 1820 communicates with theprocessor(s) 1810, 1815 via a multi-drop bus, such as a frontside bus(FSB), point-to-point interface such as QuickPath Interconnect (QPI), orsimilar connection 1895.

In one embodiment, the coprocessor 1845 is a special-purpose processor,such as, for example, a high-throughput MIC processor, a network orcommunication processor, compression engine, graphics processor, GPGPU,embedded processor, or the like. In one embodiment, controller hub 1820may include an integrated graphics accelerator.

There may be a variety of differences between the physical resources1810, 1815 in terms of a spectrum of metrics of merit includingarchitectural, microarchitectural, thermal, power consumptioncharacteristics, and the like.

In one embodiment, the processor 1810 executes instructions that controldata processing operations of a general type. Embedded within theinstructions may be coprocessor instructions. The processor 1810recognizes these coprocessor instructions as being of a type that shouldbe executed by the attached coprocessor 1845. Accordingly, the processor1810 issues these coprocessor instructions (or control signalsrepresenting coprocessor instructions) on a coprocessor bus or otherinterconnect, to coprocessor 1845. Coprocessor(s) 1845 accept andexecute the received coprocessor instructions.

Referring now to FIG. 19, shown is a block diagram of a first morespecific example system 1900 in accordance with an embodiment of thepresent invention. As shown in FIG. 19, multiprocessor system 1900 is apoint-to-point interconnect system, and includes a first processor 1970and a second processor 1980 coupled via a point-to-point interconnect1950. Each of processors 1970 and 1980 may be some version of theprocessor 1700. In one embodiment of the invention, processors 1970 and1980 are respectively processors 1810 and 1815, while coprocessor 1938is coprocessor 1845. In another embodiment, processors 1970 and 1980 arerespectively processor 1810 coprocessor 1845.

Processors 1970 and 1980 are shown including integrated memorycontroller (IMC) units 1972 and 1982, respectively. Processor 1970 alsoincludes as part of its bus controller units point-to-point (P-P)interfaces 1976 and 1978; similarly, second processor 1980 includes P-Pinterfaces 1986 and 1988. Processors 1970, 1980 may exchange informationvia a point-to-point (P-P) interface 1950 using P-P interface circuits1978, 1988. As shown in FIG. 19, IMCs 1972 and 1982 couple theprocessors to respective memories, namely a memory 1932 and a memory1934, which may be portions of main memory locally attached to therespective processors.

Processors 1970, 1980 may each exchange information with a chipset 1990via individual P-P interfaces 1952, 1954 using point to point interfacecircuits 1976, 1994, 1986, 1998. Chipset 1990 may optionally exchangeinformation with the coprocessor 1938 via a high-performance interface1939. In one embodiment, the coprocessor 1938 is a special-purposeprocessor, such as, for example, a high-throughput MIC processor, anetwork or communication processor, compression engine, graphicsprocessor, GPGPU, embedded processor, or the like.

A shared cache (not shown) may be included in either processor oroutside of both processors, yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 1990 may be coupled to a first bus 1916 via an interface 1996.In one embodiment, first bus 1916 may be a Peripheral ComponentInterconnect (PCI) bus, or a bus such as a PCI Express bus or anotherthird generation I/O interconnect bus, although the scope of the presentinvention is not so limited.

As shown in FIG. 19, various I/O devices 1914 may be coupled to firstbus 1916, along with a bus bridge 1918 which couples first bus 1916 to asecond bus 1920. In one embodiment, one or more additional processor(s)1915, such as coprocessors, high-throughput MIC processors, GPGPU's,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessor, are coupled to first bus 1916. In one embodiment, second bus1920 may be a low pin count (LPC) bus. Various devices may be coupled toa second bus 1920 including, for example, a keyboard and/or mouse 1922,communication devices 1927 and a storage unit 1928 such as a disk driveor other mass storage device which may include instructions/code anddata 1930, in one embodiment. Further, an audio I/O 1924 may be coupledto the second bus 1920. Note that other architectures are possible. Forexample, instead of the point-to-point architecture of FIG. 19, a systemmay implement a multi-drop bus or other such architecture.

Referring now to FIG. 20, shown is a block diagram of a second morespecific example system 2000 in accordance with an embodiment of thepresent invention. Like elements in FIGS. 19 and 20 bear like referencenumerals, and certain aspects of FIG. 19 have been omitted from FIG. 20in order to avoid obscuring other aspects of FIG. 20.

FIG. 20 illustrates that the processors 1970, 1980 may includeintegrated memory and I/O control logic (“CL”) 1972 and 1982,respectively. Thus, the CL 1972, 1982 include integrated memorycontroller units and include I/O control logic. FIG. 20 illustrates thatnot only are the memories 1932, 1934 coupled to the CL 1972, 1982, butalso that I/O devices 2014 are also coupled to the control logic 1972,1982. Legacy I/O devices 2015 are coupled to the chipset 1990.

Referring now to FIG. 21, shown is a block diagram of a SoC 2100 inaccordance with an embodiment of the present invention. Similar elementsin FIG. 17 bear like reference numerals. Also, dashed lined boxes areoptional features on more advanced SoCs. In FIG. 21, an interconnectunit(s) 2102 is coupled to: an application processor 2110 which includesa set of one or more cores 202A-N and shared cache unit(s) 1706; asystem agent unit 1710; a bus controller unit(s) 1716; an integratedmemory controller unit(s) 1714; a set or one or more coprocessors 2120which may include integrated graphics logic, an image processor, anaudio processor, and a video processor; an static random access memory(SRAM) unit 2130; a direct memory access (DMA) unit 2132; and a displayunit 2140 for coupling to one or more external displays. In oneembodiment, the coprocessor(s) 2120 include a special-purpose processor,such as, for example, a network or communication processor, compressionengine, GPGPU, a high-throughput MIC processor, embedded processor, orthe like.

Embodiments of the mechanisms disclosed herein may be implemented inhardware, software, firmware, or a combination of such implementationapproaches. Embodiments of the invention may be implemented as computerprograms or program code executing on programmable systems comprising atleast one processor, a storage system (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device.

Program code, such as code 1930 illustrated in FIG. 19, may be appliedto input instructions to perform the functions described herein andgenerate output information. The output information may be applied toone or more output devices, in known fashion. For purposes of thisapplication, a processing system includes any system that has aprocessor, such as, for example; a digital signal processor (DSP), amicrocontroller, an application specific integrated circuit (ASIC), or amicroprocessor.

The program code may be implemented in a high level procedural or objectoriented programming language to communicate with a processing system.The program code may also be implemented in assembly or machinelanguage, if desired. In fact, the mechanisms described herein are notlimited in scope to any particular programming language. In any case,the language may be a compiled or interpreted language.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Such machine-readable storage media may include, without limitation,non-transitory, tangible arrangements of articles manufactured or formedby a machine or device, including storage media such as hard disks, anyother type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritable's (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMs) such as dynamic random accessmemories (DRAMs), static random access memories (SRAMs), erasableprogrammable read-only memories (EPROMs), flash memories, electricallyerasable programmable read-only memories (EEPROMs), phase change memory(PCM), magnetic or optical cards, or any other type of media suitablefor storing electronic instructions.

Accordingly, embodiments of the invention also include non-transitory,tangible machine-readable media containing instructions or containingdesign data, such as Hardware Description Language (HDL), which definesstructures, circuits, apparatuses, processors and/or system featuresdescribed herein. Such embodiments may also be referred to as programproducts.

Emulation (Including Binary Translation, Code Morphing, Etc.)

In some cases, an instruction converter may be used to convert aninstruction from a source instruction set to a target instruction set.For example, the instruction converter may translate (e.g., using staticbinary translation, dynamic binary translation including dynamiccompilation), morph, emulate, or otherwise convert an instruction to oneor more other instructions to be processed by the core. The instructionconverter may be implemented in software, hardware, firmware, or acombination thereof. The instruction converter may be on processor, offprocessor, or part on and part off processor.

FIG. 22 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according to anembodiment. In the illustrated embodiment, the instruction converter isa software instruction converter, although alternatively the instructionconverter may be implemented in software, firmware, hardware, or variouscombinations thereof. FIG. 22 shows a program in a high level language2202 may be compiled using an x86 compiler 2204 to generate x86 binarycode 2206 that may be natively executed by a processor with at least onex86 instruction set core 2216. The processor with at least one x86instruction set core 2216 represents any processor that may performsubstantially the same functions as an Intel processor with at least onex86 instruction set core by compatibly executing or otherwise processing(1) a substantial portion of the instruction set of the Intel x86instruction set core or (2) object code versions of applications orother software targeted to run on an Intel processor with at least onex86 instruction set core, in order to achieve substantially the sameresult as an Intel processor with at least one x86 instruction set core.The x86 compiler 2204 represents a compiler that is operable to generatex86 binary code 2206 (e.g., object code) that may, with or withoutadditional linkage processing, be executed on the processor with atleast one x86 instruction set core 2216. Similarly, FIG. 22 shows theprogram in the high level language 2202 may be compiled using analternative instruction set compiler 2208 to generate alternativeinstruction set binary code 2210 that may be natively executed by aprocessor without at least one x86 instruction set core 2214 (e.g., aprocessor with cores that execute the MIPS instruction set of MIPSTechnologies of Sunnyvale, Calif. and/or that execute the ARMinstruction set of ARM Holdings of Sunnyvale, Calif.). The instructionconverter 2212 is used to convert the x86 binary code 2206 into codethat may be natively executed by the processor without an x86instruction set core 2214. This converted code is not likely to be thesame as the alternative instruction set binary code 2210 because aninstruction converter capable of this is difficult to make; however, theconverted code will accomplish the general operation and be made up ofinstructions from the alternative instruction set. Thus, the instructionconverter 2212 represents software, firmware, hardware, or a combinationthereof that, through emulation, simulation or any other process, allowsa processor or other electronic device that does not have an x86instruction set processor or core to execute the x86 binary code 2206.

To better illustrate the method and apparatuses disclosed herein, anon-limiting list of embodiments is provided here.

Example 1 is a hardware accelerated system comprising: a parallelcompute circuit including a first multiply and accumulate (MAC) circuitto obtain a parallel computing input and calculate a parallel MAC singlecolumn output based on the parallel computing input and the first columninput; a computation separation circuit to provide a serial computinginput to a serial compute circuit based on the parallel MAC singlecolumn output; and a serial compute circuit to obtain the serialcomputing input and calculate a serial computation output.

In Example 2, the subject matter of Example 1 optionally includes amemory access control circuit to provide the parallel computing input.

In Example 3, the subject matter of Example 2 optionally includeswherein: the first column input is received at the memory access controlcircuit from a memory circuit; and the first column output is stored ina first row buffer.

In Example 4, the subject matter of Example 3 optionally includeswherein the serial computation output is stored in-place in the memorycircuit.

In Example 5, the subject matter of any one or more of Examples 3-4optionally include wherein: the first column input is read from thememory circuit in a triangular storage format; and the serialcomputation output is stored in the memory circuit in a compressedlinear storage format.

In Example 6, the subject matter of any one or more of Examples 1-5optionally include a decomposition control circuit to calculate a matrixdecomposition control, wherein the memory access control circuit isfurther to: obtain the matrix decomposition control; and calculate theparallel computing input based on the matrix decomposition control.

In Example 7, the subject matter of any one or more of Examples 1-6optionally include wherein the decomposition control circuit is furtherto obtain a matrix decomposition configuration input, wherein the matrixdecomposition control is calculated based on the matrix decompositionconfiguration details.

In Example 8, the subject matter of any one or more of Examples 1-7optionally include wherein: the computation separation circuit isfurther to calculate a dependency clear control based on the parallelMAC single column output; and wherein calculation of the serialcomputing input is further based on the dependency clear control.

In Example 9, the subject matter of any one or more of Examples 1-8optionally include wherein the parallel compute circuit further includesa second MAC circuit to: obtain a second column input; and calculate aparallel MAC dual column output based on the second column input.

In Example 10, the subject matter of Example 9 optionally includeswherein the second column input is received from the memory circuit at asecond row buffer.

In Example 11, the subject matter of any one or more of Examples 9-10optionally include wherein the computation separation circuit is furtherto: obtain the parallel MAC dual column output; and identify an activeparallel MAC output based on the parallel MAC single column output andthe parallel MAC dual column output; wherein the computation separationcircuit calculating the serial computing input is further based on theactive parallel MAC output and the parallel MAC dual column output.

In Example 12, the subject matter of Example 11 optionally includeswherein: the parallel compute circuit is further includes a third MACcircuit to obtain a third column input and calculate a parallel MACmultiple column output based on the third column input; and thecomputation separation circuit identifying the active parallel MACoutput is further based on the parallel MAC third column output.

In Example 13, the subject matter of any one or more of Examples 1-12optionally include wherein: the serial compute circuit includes a serialsubtraction circuit to obtain the serial computing input and calculate aserial subtraction output; and the serial computation output is based onthe serial subtraction output.

In Example 14, the subject matter of Example 13 optionally includeswherein the serial compute circuit further includes a square rootcircuit to: obtain the serial subtraction output; and calculate a serialsquare root output based on the serial subtraction output; whereincalculation of the serial computation output is further based on theserial square root output.

In Example 15, the subject matter of Example 14 optionally includeswherein the serial square root output is based on a Choleskydecomposition.

In Example 16, the subject matter of any one or more of Examples 13-15optionally include wherein the serial compute circuit further includesan inverse circuit to: obtain the serial subtraction output; andcalculate a serial inverse output based on the serial subtractionoutput; wherein the calculation of the serial computation output isfurther based on the serial inverse output.

In Example 17, the subject matter of Example 16 optionally includeswherein the serial inverse output is based on a Cholesky decomposition.

In Example 18, the subject matter of any one or more of Examples 16-17optionally include wherein the serial inverse output is based on aTakagi decomposition.

In Example 19, the subject matter of any one or more of Examples 1-18optionally include wherein the memory access control circuit is furtherto: obtain the serial subtraction output; and write the serialsubtraction output to a memory.

Example 20 is a method comprising: obtaining a parallel computing inputat a parallel compute circuit; calculating a parallel MAC single columnoutput at a first multiply and accumulate (MAC) circuit within theparallel compute circuit, wherein the parallel MAC single column outputis calculated based on the parallel computing input and a first columninput; invoking a computation separation circuit to provide a serialcomputing input to a serial compute circuit based on the parallel MACsingle column output; and providing a serial computation outputcalculated by the serial compute circuit based on the serial computinginput to the memory access control circuit.

In Example 21, the subject matter of Example 20 optionally includeswherein the parallel computing input is obtained from a memory accesscontrol circuit.

In Example 22, the subject matter of any one or more of Examples 20-21optionally include obtaining the first column input at the memory accesscontrol circuit from a memory circuit; and storing the first columnoutput in a first row buffer.

In Example 23, the subject matter of Example 22 optionally includesstoring the serial computation output in-place in the memory circuit.

In Example 24, the subject matter of any one or more of Examples 22-23optionally include reading the first column input from the memorycircuit in a triangular storage format; and storing the serialcomputation output in the memory circuit in a compressed linear storageformat.

In Example 25, the subject matter of any one or more of Examples 20-24optionally include calculating a matrix decomposition control at adecomposition control circuit; and calculating the parallel computinginput at the memory access control circuit based on the matrixdecomposition control.

In Example 26, the subject matter of any one or more of Examples 20-25optionally include obtaining a matrix decomposition configuration inputat the decomposition control circuit, wherein the matrix decompositioncontrol is calculated based on the matrix decomposition configurationinput.

In Example 27, the subject matter of any one or more of Examples 20-26optionally include wherein the computation separation circuit furthercalculates a dependency clear control based on the parallel MAC singlecolumn output, wherein calculating the serial computing input is furtherbased on the dependency clear control.

In Example 28, the subject matter of any one or more of Examples 20-27optionally include calculating a parallel MAC dual column output at asecond MAC circuit within the parallel compute circuit based on thesecond column input.

In Example 29, the subject matter of Example 28 optionally includesobtaining the second column input from the memory circuit at a secondrow buffer.

In Example 30, the subject matter of any one or more of Examples 28-29optionally include identifying an active parallel MAC output at thecomputation separation circuit based on the parallel MAC single columnoutput and the parallel MAC dual column output, wherein the computationseparation circuit calculating the serial computing input is furtherbased on the active parallel MAC output and the parallel MAC dual columnoutput.

In Example 31, the subject matter of Example 30 optionally includescalculating a parallel MAC third column output at a third MAC circuitwithin the parallel compute circuit, wherein identifying the activeparallel MAC output is further based on the parallel MAC third columnoutput.

In Example 32, the subject matter of any one or more of Examples 20-31optionally include calculating a serial subtraction output at a serialsubtraction circuit within the serial compute circuit based on theserial computing input, wherein the serial computation output is basedon the serial subtraction output.

In Example 33, the subject matter of Example 32 optionally includescalculating a serial square root output at a square root circuit withinthe serial compute circuit based on the serial subtraction output,wherein the serial computation output is further based on the serialsquare root output.

In Example 34, the subject matter of Example 33 optionally includeswherein the serial square root output is based on a Choleskydecomposition.

In Example 35, the subject matter of any one or more of Examples 32-34optionally include calculating a serial inverse output at an inversecircuit within the serial compute circuit based on the serialsubtraction output, wherein the serial computation output is furtherbased on the serial inverse output.

In Example 36, the subject matter of Example 35 optionally includeswherein the serial inverse output is based on a Cholesky decomposition.

In Example 37, the subject matter of any one or more of Examples 35-36optionally include wherein the serial inverse output is based on aTakagi decomposition.

In Example 38, the subject matter of any one or more of Examples 20-37optionally include obtaining the serial subtraction output at the memoryaccess control circuit; and writing the serial subtraction output to amemory.

Example 39 is at least one machine-readable medium includinginstructions, which when executed by a computing system, cause thecomputing system to perform any of the methods of Examples 20-38.

Example 40 is an apparatus comprising means for performing any of themethods of Examples 20-38.

Example 41 is at least one non-transitory machine-readable storagemedium, comprising a plurality of instructions that, responsive to beingexecuted with processor circuitry of a computer-controlled device, causethe computer-controlled device to: obtain a parallel computing input ata parallel compute circuit; calculate a parallel MAC single columnoutput at a first multiply and accumulate (MAC) circuit within theparallel compute circuit, wherein the parallel MAC single column outputis calculated based on the parallel computing input and a first columninput; invoke a computation separation circuit to provide a serialcomputing input to a serial compute circuit based on the parallel MACsingle column output; and provide a serial computation output calculatedby the serial compute circuit based on the serial computing input to thememory access control circuit.

In Example 42, the subject matter of Example 41 optionally includeswherein the parallel computing input is obtained from a memory accesscontrol circuit.

In Example 43, the subject matter of Example 42 optionally includesinstructions to cause the computer-controlled device to: obtain thefirst column input at the memory access control circuit from a memorycircuit; and store the first column output in a first row buffer.

In Example 44, the subject matter of Example 43 optionally includesinstructions to cause the computer-controlled device to store the serialcomputation output in-place in the memory circuit.

In Example 45, the subject matter of any one or more of Examples 43-44optionally include instructions to cause the computer-controlled deviceto: read the first column input from the memory circuit in a triangularstorage format; and store the serial computation output in the memorycircuit in a compressed linear storage format.

In Example 46, the subject matter of any one or more of Examples 41-45optionally include instructions to cause the computer-controlled deviceto: calculate a matrix decomposition control at a decomposition controlcircuit; and calculate the parallel computing input at the memory accesscontrol circuit based on the matrix decomposition control.

In Example 47, the subject matter of any one or more of Examples 41-46optionally include instructions to cause the computer-controlled deviceto obtain a matrix decomposition configuration input at thedecomposition control circuit, wherein the matrix decomposition controlis calculated based on the matrix decomposition configuration input.

In Example 48, the subject matter of any one or more of Examples 41-47optionally include instructions to cause the computer-controlled deviceto calculate a dependency clear control at the computation separationcircuit based on the parallel MAC single column output, whereincalculating the serial computing input is further based on thedependency clear control.

In Example 49, the subject matter of any one or more of Examples 41-48optionally include instructions to cause the computer-controlled deviceto calculate a parallel MAC dual column output at a second MAC circuitwithin the parallel compute circuit based on the second column input.

In Example 50, the subject matter of Example 49 optionally includesinstructions to cause the computer-controlled device to identify anactive parallel MAC output at the computation separation circuit basedon the parallel MAC single column output and the parallel MAC dualcolumn output, wherein calculating the serial computing input is furtherbased on the active parallel MAC output and the parallel MAC dual columnoutput.

In Example 51, the subject matter of Example 50 optionally includesinstructions to cause the computer-controlled device to calculate aparallel MAC third column output at a third MAC circuit within theparallel compute circuit, wherein identifying the active parallel MACoutput is further based on the parallel MAC third column output.

In Example 52, the subject matter of any one or more of Examples 41-51optionally include instructions to cause the computer-controlled deviceto calculate a serial subtraction output at a serial subtraction circuitwithin the serial compute circuit based on the serial computing input,wherein calculating the serial computation output is based on the serialsubtraction output.

In Example 53, the subject matter of Example 52 optionally includesinstructions to cause the computer-controlled device to calculate aserial square root output at a square root circuit within the serialcompute circuit based on the serial subtraction output, wherein theserial computation output is further based on the serial square rootoutput.

In Example 54, the subject matter of Example 53 optionally includeswherein the serial square root output is based on a Choleskydecomposition.

In Example 55, the subject matter of any one or more of Examples 52-54optionally include instructions to cause the computer-controlled deviceto calculate a serial inverse output at an inverse circuit within theserial compute circuit based on the serial subtraction output, whereincalculating the serial computation output is further based on the serialinverse output.

In Example 56, the subject matter of Example 55 optionally includeswherein the serial inverse output is based on a Cholesky decomposition.

In Example 57, the subject matter of any one or more of Examples 55-56optionally include wherein the serial inverse output is based on aTakagi decomposition.

In Example 58, the subject matter of any one or more of Examples 41-57optionally include instructions to cause the computer-controlled deviceto: obtain the serial subtraction output at the memory access controlcircuit; and write the serial subtraction output to a memory.

Example 59 is a hardware accelerated apparatus comprising: means forobtaining a parallel computing input at a parallel compute circuit;means for calculating a parallel MAC single column output at a firstmultiply and accumulate (MAC) circuit within the parallel computecircuit, wherein the parallel MAC single column output is calculatedbased on the parallel computing input and a first column input; meansfor invoking a computation separation circuit to provide a serialcomputing input to a serial compute circuit based on the parallel MACsingle column output; and means for providing a serial computationoutput calculated by the serial compute circuit based on the serialcomputing input to the memory access control circuit.

In Example 60, the subject matter of Example 59 optionally includeswherein the means for obtaining a parallel computing input includes amemory access control circuit.

In Example 61, the subject matter of Example 60 optionally includesmeans for obtaining the first column input at the memory access controlcircuit from a memory circuit; and means for storing the first columnoutput in a first row buffer.

In Example 62, the subject matter of Example 61 optionally includesmeans for storing the serial computation output in-place in the memorycircuit.

In Example 63, the subject matter of any one or more of Examples 61-62optionally include means for reading the first column input from thememory circuit in a triangular storage format; and means for storing theserial computation output in the memory circuit in a compressed linearstorage format.

In Example 64, the subject matter of any one or more of Examples 59-63optionally include means for calculating a matrix decomposition controlat a decomposition control circuit; and means for calculating theparallel computing input at the memory access control circuit based onthe matrix decomposition control.

In Example 65, the subject matter of any one or more of Examples 59-64optionally include means for obtaining a matrix decompositionconfiguration input at the decomposition control circuit, wherein thematrix decomposition control is calculated based on the matrixdecomposition configuration input.

In Example 66, the subject matter of any one or more of Examples 59-65optionally include means for calculating a dependency clear control atthe computation separation circuit based on the parallel MAC singlecolumn output, wherein means for calculating the serial computing inputis further based on the dependency clear control.

In Example 67, the subject matter of any one or more of Examples 59-66optionally include means for calculate a parallel MAC dual column outputat a second MAC circuit within the parallel compute circuit based on thesecond column input.

In Example 68, the subject matter of Example 67 optionally includesmeans for identifying an active parallel MAC output at the computationseparation circuit based on the parallel MAC single column output andthe parallel MAC dual column output, wherein the means for calculatingthe serial computing input is further based on the active parallel MACoutput and the parallel MAC dual column output.

In Example 69, the subject matter of Example 68 optionally includesmeans for calculating a parallel MAC third column output at a third MACcircuit within the parallel compute circuit, wherein identifying theactive parallel MAC output is further based on the parallel MAC thirdcolumn output.

In Example 70, the subject matter of any one or more of Examples 59-69optionally include means for calculating a serial subtraction output ata serial subtraction circuit within the serial compute circuit based onthe serial computing input, wherein the means for calculating the serialcomputation output is based on the serial subtraction output.

In Example 71, the subject matter of Example 70 optionally includesmeans for calculating a serial square root output at a square rootcircuit within the serial compute circuit based on the serialsubtraction output, wherein the means for calculating the serialcomputation output is further based on the serial square root output.

In Example 72, the subject matter of Example 71 optionally includeswherein the serial square root output is based on a Choleskydecomposition.

In Example 73, the subject matter of any one or more of Examples 70-72optionally include means for calculating a serial inverse output at aninverse circuit within the serial compute circuit based on the serialsubtraction output, wherein the means for calculating the serialcomputation output is further based on the serial inverse output.

In Example 74, the subject matter of Example 73 optionally includeswherein the serial inverse output is based on a Cholesky decomposition.

In Example 75, the subject matter of any one or more of Examples 73-74optionally include wherein the serial inverse output is based on aTakagi decomposition.

In Example 76, the subject matter of any one or more of Examples 59-75optionally include means for obtaining the serial subtraction output atthe memory access control circuit; and means for writing the serialsubtraction output to a memory.

Example 77 is at least one machine-readable medium includinginstructions, which when executed by a machine, cause the machine toperform operations of any of the operations of Examples 1-76.

Example 78 is an apparatus comprising means for performing any of theoperations of Examples 1-76.

Example 79 is a system to perform the operations of any of the Examples1-76.

Example 80 is a method to perform the operations of any of the Examples1-76.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which the subjectmatter may be practiced. These embodiments are also referred to hereinas “examples.” Such examples may include elements in addition to thoseshown or described. However, the present inventors also contemplateexamples in which only those elements shown or described are provided.Moreover, the present inventors also contemplate examples using anycombination or permutation of those elements shown or described (or oneor more aspects thereof), either with respect to a particular example(or one or more aspects thereof), or with respect to other examples (orone or more aspects thereof) shown or described herein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to allowthe reader to quickly ascertain the nature of the technical disclosure.It is submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. In the aboveDetailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment, and it is contemplated that suchembodiments may be combined with each other in various combinations orpermutations. The scope should be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled.

1. A hardware accelerated system comprising: a parallel compute circuitincluding a first multiply and accumulate (MAC) circuit to obtain aparallel computing input and calculate a parallel MAC single columnoutput based on the parallel computing input and the first column input;a computation separation circuit to provide a serial computing input toa serial compute circuit based on the parallel MAC single column output;and a serial compute circuit to obtain the serial computing input andcalculate a serial computation output.
 2. The system of claim 1, furtherincluding a memory access control circuit to provide the parallelcomputing input.
 3. The system of claim 2, wherein: the first columninput is received at the memory access control circuit from a memorycircuit; and the first column output is stored in a first row buffer. 4.The system of claim 3, wherein the serial computation output is storedin-place in the memory circuit.
 5. The system of claim 3, wherein: thefirst column input is read from the memory circuit in a triangularstorage format; and the serial computation output is stored in thememory circuit in a compressed linear storage format.
 6. The system ofclaim 1, further including a decomposition control circuit to calculatea matrix decomposition control, wherein the memory access controlcircuit is further to: obtain the matrix decomposition control; andcalculate the parallel computing input based on the matrix decompositioncontrol.
 7. The system of claim 1, wherein the decomposition controlcircuit is further to obtain a matrix decomposition configuration input,wherein the matrix decomposition control is calculated based on thematrix decomposition configuration details.
 8. The system of claim 1,wherein: the computation separation circuit is further to calculate adependency clear control based on the parallel MAC single column output;and wherein calculation of the serial computing input is further basedon the dependency clear control.
 9. The system of claim 1, wherein theparallel compute circuit further includes a second MAC circuit to:obtain a second column input; and calculate a parallel MAC dual columnoutput based on the second column input.
 10. The system of claim 9,wherein the second column input is received from the memory circuit at asecond row buffer.
 11. The system of claim 9, wherein the computationseparation circuit is further to: obtain the parallel MAC dual columnoutput; and identify an active parallel MAC output based on the parallelMAC single column output and the parallel MAC dual column output;wherein the computation separation circuit calculating the serialcomputing input is further based on the active parallel MAC output andthe parallel MAC dual column output.
 12. The system of claim 11,wherein: the parallel compute circuit is further includes a third MACcircuit to obtain a third column input and calculate a parallel MACmultiple column output based on the third column input; and thecomputation separation circuit identifying the active parallel MACoutput is further based on the parallel MAC third column output.
 13. Thesystem of claim 1, wherein: the serial compute circuit includes a serialsubtraction circuit to obtain the serial computing input and calculate aserial subtraction output; and the serial computation output is based onthe serial subtraction output.
 14. The system of claim 13, wherein theserial compute circuit further includes a square root circuit to: obtainthe serial subtraction output; and calculate a serial square root outputbased on the serial subtraction output; wherein calculation of theserial computation output is further based on the serial square rootoutput.
 15. The system of claim 13, wherein the serial compute circuitfurther includes an inverse circuit to: obtain the serial subtractionoutput; and calculate a serial inverse output based on the serialsubtraction output; wherein the calculation of the serial computationoutput is further based on the serial inverse output.
 16. The system ofclaim 1, wherein the memory access control circuit is further to: obtainthe serial subtraction output; and write the serial subtraction outputto a memory.
 17. A method comprising: obtaining a parallel computinginput at a parallel compute circuit; calculating a parallel MAC singlecolumn output at a first multiply and accumulate (MAC) circuit withinthe parallel compute circuit, wherein the parallel MAC single columnoutput is calculated based on the parallel computing input and a firstcolumn input; invoking a computation separation circuit to provide aserial computing input to a serial compute circuit based on the parallelMAC single column output; and providing a serial computation outputcalculated by the serial compute circuit based on the serial computinginput to the memory access control circuit.
 18. The method of claim 17,wherein the parallel computing input is obtained from a memory accesscontrol circuit.
 19. The method of claim 17, further including:obtaining the first column input at the memory access control circuitfrom a memory circuit; and storing the first column output in a firstrow buffer.
 20. The method of claim 19, further including storing theserial computation output in-place in the memory circuit.
 21. The methodof claim 19, further including: reading the first column input from thememory circuit in a triangular storage format; and storing the serialcomputation output in the memory circuit in a compressed linear storageformat.
 22. The method of claim 17, further including: calculating amatrix decomposition control at a decomposition control circuit; andcalculating the parallel computing input at the memory access controlcircuit based on the matrix decomposition control.
 23. The method ofclaim 17, further including obtaining a matrix decompositionconfiguration input at the decomposition control circuit, wherein thematrix decomposition control is calculated based on the matrixdecomposition configuration input.
 24. The method of claim 17, whereinthe computation separation circuit further calculates a dependency clearcontrol based on the parallel MAC single column output, whereincalculating the serial computing input is further based on thedependency clear control.
 25. The method of claim 17, further includingcalculating a parallel MAC dual column output at a second MAC circuitwithin the parallel compute circuit based on the second column input.